diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index fafe6faca..bacd4f014 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -139,6 +139,8 @@ title: QED Math - local: environments/reasoning_gym title: Reasoning Gym + - local: environments/spatial_gym + title: Spatial-Gym - local: environments/tau2 title: τ²-bench - local: environments/tbench2 diff --git a/docs/source/environments.md b/docs/source/environments.md index efee769cd..6da9a09fc 100644 --- a/docs/source/environments.md +++ b/docs/source/environments.md @@ -229,6 +229,13 @@ The OpenEnv community has built a catalog of ready-to-run environments that cove 📄 Docs +
+
Spatial-Gym
+

SPaRC grid pathfinding puzzles solved one move per step, with the Spatial-Gym paper's prompts and a 1.0 reward for a line that follows every rule.

+
+ 📄 Docs +
+
REPL

Python REPL environment for code execution tasks based on the Recursive Language Models paradigm with sandboxed execution and context loading.

diff --git a/docs/source/environments/spatial_gym.md b/docs/source/environments/spatial_gym.md new file mode 100644 index 000000000..544568071 --- /dev/null +++ b/docs/source/environments/spatial_gym.md @@ -0,0 +1,118 @@ + +# Spatial-Gym Environment + +[Spatial-Gym](https://github.com/lkaesberg/Spatial-Gym) turns the [SPaRC](https://sparc.gipplab.org/) grid puzzles into a step-by-step environment. The agent draws one line from the start node to the exit, one move at a time, and the line has to follow every rule on the board. In the [Spatial-Gym paper](https://arxiv.org/abs/2604.09338), humans solve 98.0% of the test puzzles, and the best model, GPT-OSS 120B, solves 16.0%. + +![Spatial-Gym web playground](https://raw.githubusercontent.com/lkaesberg/Spatial-Gym/main/environments/spatial_gym_openenv/assets/spatial-gym-playground.png) + +*The `/web` playground mid-episode: the board with the line so far, and one button per legal move.* + +This environment serves Spatial-Gym through OpenEnv. It uses the paper's prompts and scoring, with the polyshape and negative polyshape rules stated the way the puzzles were built, and the reward matches the [Prime Intellect version](https://github.com/lkaesberg/Spatial-Gym/tree/main/environments/spatial_gym). + +## One episode + +- `reset()` loads one puzzle. The observation holds the board, the legal moves, the paper's rules prompt (`system_prompt`) and the paper's message for the first move (`prompt`). +- `step(SpatialGymAction(action=...))` makes one move: `0`=RIGHT, `1`=UP, `2`=LEFT, `3`=DOWN. A move that is not legal leaves the line where it is but still counts as a step. +- The episode ends when the line reaches the exit, when no legal move is left, or after `max_steps` moves. +- The reward is `1.0` when the episode ends with a line that exactly matches one of the puzzle's stored solutions, and `0.0` otherwise. `metadata["rule_status"]` shows which rules the line meets so far. + +![A SPaRC puzzle solved one move per step](https://raw.githubusercontent.com/lkaesberg/Spatial-Gym/main/environments/spatial_gym_openenv/assets/spatial-gym-solve.gif) + +*A level-5 puzzle solved move by move: the line starts at the big circle and has to reach the exit at the bottom left while following every rule on the board.* + +## Quick start + +Install the client: + +```bash +pip install "git+https://github.com/openenv/OpenEnv.git#subdirectory=envs/spatial_gym_env" +``` + +Connect to a running server, then let a model play one puzzle: + +```python +from spatial_gym_env import SpatialGymAction, SpatialGymEnv + +with SpatialGymEnv(base_url="http://localhost:8000").sync() as env: + result = env.reset(difficulty=2, seed=0) + messages = [ + {"role": "system", "content": result.observation.system_prompt}, + {"role": "user", "content": result.observation.prompt}, + ] + while not result.done: + reply = my_model( + messages + ) # your LLM call; the reply ends with "Final: " + messages.append({"role": "assistant", "content": reply}) + result = env.step(SpatialGymAction.from_reply(reply)) + if not result.done: + messages.append({"role": "user", "content": result.observation.prompt}) + print("solved" if result.reward == 1.0 else "not solved") +``` + +`SpatialGymAction.from_reply` reads the move the way the paper did and raises `ValueError` when the reply has none. To run the environment from its Hugging Face Space image with Docker, use `SpatialGymEnv.from_env("")` instead of `base_url`. + +## Reset options + +| Option | Default | Meaning | +|---|---|---| +| `puzzle_id` | none | Play this SPaRC puzzle | +| `difficulty` | all levels | A level from 1 to 5, or a list of levels, to draw the puzzle from | +| `seed` | none | Makes the draw repeatable | +| `split` | `"test"` | `"test"` holds the 500 benchmark puzzles, `"train"` 500 more for training | +| `backtracking` | `False` | Allow stepping back onto the previous node to undo a move | +| `max_steps` | `100` | The episode ends after this many moves, as in the paper | + +## Observation + +| Field | Meaning | +|---|---| +| `puzzle_id`, `difficulty` | The puzzle and its level | +| `step` | Moves taken so far | +| `position` | Current node as `[x, y]` | +| `path` | The line so far, as `[x, y]` nodes from the start | +| `legal_actions` | Moves allowed from here | +| `grid` | Board rows in SPaRC notation; `V` marks visited nodes and `L` the current one | +| `system_prompt` | Rules and symbol legend from the paper, sent on reset only | +| `prompt` | The paper's message for the next move, empty once the episode is over | +| `solved`, `reached_end` | The line matches a stored solution; the line ended on the exit | +| `done`, `reward` | Episode over; 1.0 for a solved puzzle, else 0.0 | + +`state()` returns the episode's puzzle, options, step count and the full path. + +## Run it yourself + +From `envs/spatial_gym_env/`, start the server with `uv run --project . server` (port 8000; set `ENABLE_WEB_INTERFACE=true` for the playground at `/web`). Build the image with `openenv build`, and deploy a Space with `openenv push` (or `openenv push --repo-id my-org/spatial-gym-env`). See the [`openenv push` reference](https://huggingface.co/docs/openenv/reference/cli#openenv-push) for all options. + +The tests download the SPaRC dataset and skip themselves when Spatial-Gym is not installed. From the repository root: + +```bash +PYTHONPATH=src:envs uv run --with "Spatial-Gym>=0.1.5" pytest tests/envs/test_spatial_gym_environment.py -v +``` + +The server keeps one environment per WebSocket session, up to `MAX_CONCURRENT_ENVS` (default 8). It downloads the SPaRC dataset from Hugging Face on the first reset of each split. + +## Citation + +```bibtex +@misc{kaesberg2026spatialgym, + title = {Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym}, + author = {Kaesberg, Lars Benedikt and Yang, Tianyu and Bauer, Niklas and Ruas, Terry and Wahle, Jan Philip and Gipp, Bela}, + year = {2026}, + eprint = {2604.09338}, + archivePrefix = {arXiv} +} + +@inproceedings{kaesberg-etal-2025-sparc, + title = "{SP}a{RC}: A Spatial Pathfinding Reasoning Challenge", + author = "Kaesberg, Lars Benedikt and Wahle, Jan Philip and Ruas, Terry and Gipp, Bela", + booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing", + month = nov, + year = "2025", + address = "Suzhou, China", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2025.emnlp-main.526/", + doi = "10.18653/v1/2025.emnlp-main.526", + pages = "10359--10390" +} +``` diff --git a/envs/spatial_gym_env/.dockerignore b/envs/spatial_gym_env/.dockerignore new file mode 100644 index 000000000..dc3081923 --- /dev/null +++ b/envs/spatial_gym_env/.dockerignore @@ -0,0 +1,5 @@ +.venv/ +__pycache__/ +*.pyc +.pytest_cache/ +tests/ diff --git a/envs/spatial_gym_env/README.md b/envs/spatial_gym_env/README.md new file mode 100644 index 000000000..490e851df --- /dev/null +++ b/envs/spatial_gym_env/README.md @@ -0,0 +1,134 @@ +--- +title: Spatial-Gym Environment Server +emoji: 🧩 +colorFrom: green +colorTo: gray +sdk: docker +pinned: false +app_port: 8000 +base_path: /web +short_description: SPaRC grid pathfinding puzzles, one move per step +tags: + - openenv + - reinforcement-learning + - spatial-reasoning + - puzzles +--- + +# Spatial-Gym Environment + +[Spatial-Gym](https://github.com/lkaesberg/Spatial-Gym) turns the [SPaRC](https://sparc.gipplab.org/) grid puzzles into a step-by-step environment. The agent draws one line from the start node to the exit, one move at a time, and the line has to follow every rule on the board. In the [Spatial-Gym paper](https://arxiv.org/abs/2604.09338), humans solve 98.0% of the test puzzles, and the best model, GPT-OSS 120B, solves 16.0%. + +![Spatial-Gym web playground](https://raw.githubusercontent.com/lkaesberg/Spatial-Gym/main/environments/spatial_gym_openenv/assets/spatial-gym-playground.png) + +*The `/web` playground mid-episode: the board with the line so far, and one button per legal move.* + +This environment serves Spatial-Gym through OpenEnv. It uses the paper's prompts and scoring, with the polyshape and negative polyshape rules stated the way the puzzles were built, and the reward matches the [Prime Intellect version](https://github.com/lkaesberg/Spatial-Gym/tree/main/environments/spatial_gym). + +## One episode + +- `reset()` loads one puzzle. The observation holds the board, the legal moves, the paper's rules prompt (`system_prompt`) and the paper's message for the first move (`prompt`). +- `step(SpatialGymAction(action=...))` makes one move: `0`=RIGHT, `1`=UP, `2`=LEFT, `3`=DOWN. A move that is not legal leaves the line where it is but still counts as a step. +- The episode ends when the line reaches the exit, when no legal move is left, or after `max_steps` moves. +- The reward is `1.0` when the episode ends with a line that exactly matches one of the puzzle's stored solutions, and `0.0` otherwise. `metadata["rule_status"]` shows which rules the line meets so far. + +![A SPaRC puzzle solved one move per step](https://raw.githubusercontent.com/lkaesberg/Spatial-Gym/main/environments/spatial_gym_openenv/assets/spatial-gym-solve.gif) + +*A level-5 puzzle solved move by move: the line starts at the big circle and has to reach the exit at the bottom left while following every rule on the board.* + +## Quick start + +Install the client: + +```bash +pip install "git+https://github.com/openenv/OpenEnv.git#subdirectory=envs/spatial_gym_env" +``` + +Connect to a running server, then let a model play one puzzle: + +```python +from spatial_gym_env import SpatialGymAction, SpatialGymEnv + +with SpatialGymEnv(base_url="http://localhost:8000").sync() as env: + result = env.reset(difficulty=2, seed=0) + messages = [ + {"role": "system", "content": result.observation.system_prompt}, + {"role": "user", "content": result.observation.prompt}, + ] + while not result.done: + reply = my_model( + messages + ) # your LLM call; the reply ends with "Final: " + messages.append({"role": "assistant", "content": reply}) + result = env.step(SpatialGymAction.from_reply(reply)) + if not result.done: + messages.append({"role": "user", "content": result.observation.prompt}) + print("solved" if result.reward == 1.0 else "not solved") +``` + +`SpatialGymAction.from_reply` reads the move the way the paper did and raises `ValueError` when the reply has none. To run the environment from its Hugging Face Space image with Docker, use `SpatialGymEnv.from_env("")` instead of `base_url`. + +## Reset options + +| Option | Default | Meaning | +|---|---|---| +| `puzzle_id` | none | Play this SPaRC puzzle | +| `difficulty` | all levels | A level from 1 to 5, or a list of levels, to draw the puzzle from | +| `seed` | none | Makes the draw repeatable | +| `split` | `"test"` | `"test"` holds the 500 benchmark puzzles, `"train"` 500 more for training | +| `backtracking` | `False` | Allow stepping back onto the previous node to undo a move | +| `max_steps` | `100` | The episode ends after this many moves, as in the paper | + +## Observation + +| Field | Meaning | +|---|---| +| `puzzle_id`, `difficulty` | The puzzle and its level | +| `step` | Moves taken so far | +| `position` | Current node as `[x, y]` | +| `path` | The line so far, as `[x, y]` nodes from the start | +| `legal_actions` | Moves allowed from here | +| `grid` | Board rows in SPaRC notation; `V` marks visited nodes and `L` the current one | +| `system_prompt` | Rules and symbol legend from the paper, sent on reset only | +| `prompt` | The paper's message for the next move, empty once the episode is over | +| `solved`, `reached_end` | The line matches a stored solution; the line ended on the exit | +| `done`, `reward` | Episode over; 1.0 for a solved puzzle, else 0.0 | + +`state()` returns the episode's puzzle, options, step count and the full path. + +## Run it yourself + +From `envs/spatial_gym_env/`, start the server with `uv run --project . server` (port 8000; set `ENABLE_WEB_INTERFACE=true` for the playground at `/web`). Build the image with `openenv build`, and deploy a Space with `openenv push` (or `openenv push --repo-id my-org/spatial-gym-env`). See the [`openenv push` reference](https://huggingface.co/docs/openenv/reference/cli#openenv-push) for all options. + +The tests download the SPaRC dataset and skip themselves when Spatial-Gym is not installed. From the repository root: + +```bash +PYTHONPATH=src:envs uv run --with "Spatial-Gym>=0.1.5" pytest tests/envs/test_spatial_gym_environment.py -v +``` + +The server keeps one environment per WebSocket session, up to `MAX_CONCURRENT_ENVS` (default 8). It downloads the SPaRC dataset from Hugging Face on the first reset of each split. + +## Citation + +```bibtex +@misc{kaesberg2026spatialgym, + title = {Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym}, + author = {Kaesberg, Lars Benedikt and Yang, Tianyu and Bauer, Niklas and Ruas, Terry and Wahle, Jan Philip and Gipp, Bela}, + year = {2026}, + eprint = {2604.09338}, + archivePrefix = {arXiv} +} + +@inproceedings{kaesberg-etal-2025-sparc, + title = "{SP}a{RC}: A Spatial Pathfinding Reasoning Challenge", + author = "Kaesberg, Lars Benedikt and Wahle, Jan Philip and Ruas, Terry and Gipp, Bela", + booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing", + month = nov, + year = "2025", + address = "Suzhou, China", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2025.emnlp-main.526/", + doi = "10.18653/v1/2025.emnlp-main.526", + pages = "10359--10390" +} +``` diff --git a/envs/spatial_gym_env/__init__.py b/envs/spatial_gym_env/__init__.py new file mode 100644 index 000000000..7e5ce4ec6 --- /dev/null +++ b/envs/spatial_gym_env/__init__.py @@ -0,0 +1,17 @@ +"""Spatial-Gym for OpenEnv: SPaRC pathfinding puzzles, solved one move per step.""" + +from .client import SpatialGymEnv +from .models import ( + SpatialGymAction, + SpatialGymObservation, + SpatialGymState, + parse_action, +) + +__all__ = [ + "SpatialGymAction", + "SpatialGymEnv", + "SpatialGymObservation", + "SpatialGymState", + "parse_action", +] diff --git a/envs/spatial_gym_env/client.py b/envs/spatial_gym_env/client.py new file mode 100644 index 000000000..302642887 --- /dev/null +++ b/envs/spatial_gym_env/client.py @@ -0,0 +1,49 @@ +"""Client for the Spatial-Gym OpenEnv server (one WebSocket session per client).""" + +from typing import Any, Dict + +from openenv.core.client_types import StepResult +from openenv.core.env_client import EnvClient + +try: + from .models import SpatialGymAction, SpatialGymObservation, SpatialGymState +except ImportError: + from models import SpatialGymAction, SpatialGymObservation, SpatialGymState + + +class SpatialGymEnv( + EnvClient[SpatialGymAction, SpatialGymObservation, SpatialGymState] +): + """Connect to a running server, a Docker image or a Hugging Face Space. + + >>> with SpatialGymEnv(base_url="http://localhost:8000").sync() as env: + ... result = env.reset(difficulty=1, seed=0) + ... result = env.step(SpatialGymAction(action=0)) + """ + + def _step_payload(self, action: SpatialGymAction) -> Dict[str, Any]: + return {"action": action.action} + + def _parse_result( + self, payload: Dict[str, Any] + ) -> StepResult[SpatialGymObservation]: + fields = dict(payload.get("observation", {})) + fields["done"] = payload.get("done", False) + fields["reward"] = payload.get("reward") + fields["metadata"] = fields.get("metadata") or payload.get("metadata") or {} + observation = SpatialGymObservation( + **{ + k: v + for k, v in fields.items() + if k in SpatialGymObservation.model_fields + } + ) + return StepResult( + observation=observation, + reward=payload.get("reward"), + done=payload.get("done", False), + metadata=payload.get("metadata"), + ) + + def _parse_state(self, payload: Dict[str, Any]) -> SpatialGymState: + return SpatialGymState(**payload) diff --git a/envs/spatial_gym_env/discovery.json b/envs/spatial_gym_env/discovery.json new file mode 100644 index 000000000..51f7a58c8 --- /dev/null +++ b/envs/spatial_gym_env/discovery.json @@ -0,0 +1,9 @@ +{ + "description": "Multi-step spatial reasoning: draw one line through a SPaRC grid puzzle, one move per step, from the start node to the exit while following every rule on the board. The reward is 1.0 for a line that matches a stored solution.", + "tags": ["openenv", "rl-environment", "spatial-reasoning", "planning", "puzzles", "multi-turn", "grid-world"], + "representative_queries": [ + "find a spatial reasoning environment for language models", + "evaluate step-by-step planning on grid pathfinding puzzles", + "find a multi-turn puzzle environment with a verifiable reward" + ] +} diff --git a/envs/spatial_gym_env/models.py b/envs/spatial_gym_env/models.py new file mode 100644 index 000000000..0943f0f8d --- /dev/null +++ b/envs/spatial_gym_env/models.py @@ -0,0 +1,112 @@ +"""Data models for the Spatial-Gym OpenEnv environment. + +One episode is one SPaRC puzzle. The agent draws the line one move at a time, starting +at the start node, and the episode ends when it reaches the exit, runs out of legal +moves, or hits the step limit. +""" + +import re +from typing import List, Optional + +from openenv.core.env_server.types import Action, Observation, State +from pydantic import Field + +DIRECTION_WORD_TO_ACTION = {"right": 0, "up": 1, "left": 2, "down": 3} + + +def parse_action(reply: str) -> int: + """Parse an action from the model's reply. + + Accepted formats (case-insensitive): + Final: 2 (digit 0-3) + Final: left (direction word) + Final: LEFT (direction word, any case) + Falls back to a bare digit or direction word at the end of the reply. + """ + # Primary: "Final: " or "Final: " + m = re.search(r"Final:\s*([0-3])\b", reply, re.IGNORECASE) + if m: + return int(m.group(1)) + + m = re.search(r"Final:\s*(right|up|left|down)\b", reply, re.IGNORECASE) + if m: + return DIRECTION_WORD_TO_ACTION[m.group(1).lower()] + + # Fallback: standalone digit 0-3 at the end + m = re.search(r"\b([0-3])\s*$", reply) + if m: + return int(m.group(1)) + + # Fallback: standalone direction word at the end + m = re.search(r"\b(right|up|left|down)\s*$", reply, re.IGNORECASE) + if m: + return DIRECTION_WORD_TO_ACTION[m.group(1).lower()] + + raise ValueError( + f"Invalid model output, no 'Final: <0-3|right|up|left|down>' found in response: {reply}" + ) + + +class SpatialGymAction(Action): + """One move of the line: 0=RIGHT, 1=UP, 2=LEFT, 3=DOWN.""" + + action: int = Field(..., ge=0, le=3, description="0=RIGHT, 1=UP, 2=LEFT, 3=DOWN") + + @classmethod + def from_reply(cls, reply: str) -> "SpatialGymAction": + """Read the move from a model reply that ends with `Final: ` (paper format). + + Raises ValueError when the reply holds no move. + """ + return cls(action=parse_action(reply.split("")[-1].strip())) + + +class SpatialGymObservation(Observation): + """The board after the last move. `done`, `reward` and `metadata` come from Observation.""" + + puzzle_id: str = Field(default="", description="SPaRC puzzle id") + difficulty: int = Field( + default=0, description="Difficulty level, 1 (easy) to 5 (hard)" + ) + step: int = Field(default=0, description="Moves taken so far") + position: List[int] = Field( + default_factory=list, description="Current node as [x, y]" + ) + path: List[List[int]] = Field( + default_factory=list, + description="The line so far, as [x, y] nodes from the start", + ) + legal_actions: List[int] = Field( + default_factory=list, description="Moves allowed from here" + ) + grid: List[List[str]] = Field( + default_factory=list, + description="Board rows in SPaRC notation; V marks visited nodes and L the current one", + ) + system_prompt: str = Field( + default="", + description="Rules and symbol legend, as in the paper (sent on reset only)", + ) + prompt: str = Field(default="", description="The paper's message for the next move") + solved: bool = Field( + default=False, description="The line matches a stored solution" + ) + reached_end: bool = Field( + default=False, description="The line ended on the exit node" + ) + + +class SpatialGymState(State): + """Episode state. `episode_id` and `step_count` come from State.""" + + puzzle_id: str = "" + difficulty: int = 0 + split: str = "test" + backtracking: bool = False + max_steps: int = 100 + path: List[List[int]] = Field( + default_factory=list, description="Nodes of the line as [x, y]" + ) + done: bool = False + solved: bool = False + seed: Optional[int] = None diff --git a/envs/spatial_gym_env/openenv.yaml b/envs/spatial_gym_env/openenv.yaml new file mode 100644 index 000000000..6412e10eb --- /dev/null +++ b/envs/spatial_gym_env/openenv.yaml @@ -0,0 +1,22 @@ +spec_version: 1 +name: spatial_gym_env +version: 0.1.0 +type: space +runtime: fastapi +app: server.app:app +port: 8000 +validation: + reward: + range: [0.0, 1.0] + oracle_tolerance: 0.0 + floor_margin: 0.1 + resources: + cpu: 1.0 + memory_mb: 2048 + disk_mb: 1024 + episode_timeout_s: 600.0 + capabilities: + verifier: + kind: reward_channel + types: + tags: [spatial-reasoning, planning, puzzles, multi-turn] diff --git a/envs/spatial_gym_env/pyproject.toml b/envs/spatial_gym_env/pyproject.toml new file mode 100644 index 000000000..1e9f7d752 --- /dev/null +++ b/envs/spatial_gym_env/pyproject.toml @@ -0,0 +1,36 @@ +[build-system] +requires = ["setuptools>=45", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "openenv-spatial_gym_env" +version = "0.1.0" +description = "Spatial-Gym for OpenEnv: SPaRC grid pathfinding puzzles, solved one move per step" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "openenv>=0.8.0", + "Spatial-Gym>=0.1.5", + "datasets>=3.6.0", + "gymnasium>=0.28.1", + "fastapi>=0.115.0", + "uvicorn>=0.24.0", + "pydantic>=2.0.0", +] + +[project.optional-dependencies] +dev = ["pytest>=8.0.0"] + +[project.urls] +Homepage = "https://sparc.gipplab.org/" +Repository = "https://github.com/lkaesberg/Spatial-Gym" +Paper = "https://arxiv.org/abs/2604.09338" + +[project.scripts] +# uv run --project . server +server = "spatial_gym_env.server.app:main" + +[tool.setuptools] +include-package-data = true +packages = ["spatial_gym_env", "spatial_gym_env.server"] +package-dir = { "spatial_gym_env" = ".", "spatial_gym_env.server" = "server" } diff --git a/envs/spatial_gym_env/server/Dockerfile b/envs/spatial_gym_env/server/Dockerfile new file mode 100644 index 000000000..d2e801034 --- /dev/null +++ b/envs/spatial_gym_env/server/Dockerfile @@ -0,0 +1,87 @@ +# SPDX-License-Identifier: BSD-3-Clause + +# Multi-stage build using openenv-base +# This Dockerfile is flexible and works for both: +# - In-repo environments (with local OpenEnv sources) +# - Standalone environments (with openenv from PyPI/Git) +# The build script (openenv build) handles context detection and sets appropriate build args. + +ARG BASE_IMAGE=ghcr.io/huggingface/openenv-base:latest +FROM ${BASE_IMAGE} AS builder + +WORKDIR /app + +# Ensure git is available (required for installing dependencies from VCS) +RUN apt-get update && \ + apt-get install -y --no-install-recommends git && \ + rm -rf /var/lib/apt/lists/* + +# Build argument to control whether we're building standalone or in-repo +ARG BUILD_MODE=in-repo +ARG ENV_NAME=spatial_gym_env + +# Copy environment code (always at root of build context) +COPY . /app/env + +# For in-repo builds, openenv is already vendored in the build context +# For standalone builds, openenv will be installed via pyproject.toml +WORKDIR /app/env + +# Ensure uv is available (for local builds where base image lacks it) +RUN if ! command -v uv >/dev/null 2>&1; then \ + curl -LsSf https://astral.sh/uv/install.sh | sh && \ + mv /root/.local/bin/uv /usr/local/bin/uv && \ + mv /root/.local/bin/uvx /usr/local/bin/uvx; \ + fi + +# Install dependencies using uv sync +# If uv.lock exists, use it; otherwise resolve on the fly +RUN --mount=type=cache,target=/root/.cache/uv \ + if [ -f uv.lock ]; then \ + uv sync --frozen --no-install-project --no-editable; \ + else \ + uv sync --no-install-project --no-editable; \ + fi + +RUN --mount=type=cache,target=/root/.cache/uv \ + if [ -f uv.lock ]; then \ + uv sync --frozen --no-editable; \ + else \ + uv sync --no-editable; \ + fi + +# Move the venv out of /app/env so it isn't nested inside the directory we +# copy wholesale below. Copying /app/env/.venv separately AND /app/env would +# otherwise ship the venv twice (once at /app/.venv, once at +# /app/env/.venv), roughly doubling the image size. +RUN mv /app/env/.venv /app/.venv + +# Final runtime stage +FROM ${BASE_IMAGE} + +WORKDIR /app + +# Copy the virtual environment from builder +COPY --from=builder /app/.venv /app/.venv + +# Copy the environment code (no longer contains .venv, see builder stage) +COPY --from=builder /app/env /app/env + +# Recreate a .venv symlink inside the env directory for tools (e.g. `uv run`, +# editors) that expect the virtual environment alongside the project, without +# storing a second physical copy of it. +RUN ln -s /app/.venv /app/env/.venv + +# Set PATH to use the virtual environment +ENV PATH="/app/.venv/bin:$PATH" + +# Set PYTHONPATH so imports work correctly +ENV PYTHONPATH="/app/env:$PYTHONPATH" + +# Health check +HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ + CMD curl -f http://localhost:8000/health || exit 1 + +# Run the FastAPI server +# The module path is constructed to work with the /app/env structure +CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 8000"] diff --git a/envs/spatial_gym_env/server/__init__.py b/envs/spatial_gym_env/server/__init__.py new file mode 100644 index 000000000..6c59ad0cf --- /dev/null +++ b/envs/spatial_gym_env/server/__init__.py @@ -0,0 +1,5 @@ +"""Spatial-Gym OpenEnv server components.""" + +from .spatial_gym_environment import SpatialGymEnvironment + +__all__ = ["SpatialGymEnvironment"] diff --git a/envs/spatial_gym_env/server/app.py b/envs/spatial_gym_env/server/app.py new file mode 100644 index 000000000..eef67800d --- /dev/null +++ b/envs/spatial_gym_env/server/app.py @@ -0,0 +1,54 @@ +"""FastAPI app that serves Spatial-Gym over HTTP and WebSocket (OpenEnv). + +Endpoints: POST /reset, POST /step, GET /state, GET /schema, WS /ws (one environment per +session), and the web UI at /web when ENABLE_WEB_INTERFACE=true. + +Run locally: + uv run --project . server + uv run --project . uvicorn server.app:app --port 8001 +""" + +import os +from pathlib import Path + +from openenv.core.env_server.http_server import create_app + +try: + from ..models import SpatialGymAction, SpatialGymObservation, SpatialGymState + from .spatial_gym_environment import SpatialGymEnvironment +except ImportError: + from models import SpatialGymAction, SpatialGymObservation, SpatialGymState + from server.spatial_gym_environment import SpatialGymEnvironment + + +# The web UI shows this README; by default it only looks at /app/README.md. +os.environ.setdefault( + "ENV_README_PATH", str(Path(__file__).resolve().parent.parent / "README.md") +) + +app = create_app( + SpatialGymEnvironment, + SpatialGymAction, + SpatialGymObservation, + # Also the import name the web UI's Quick Start shows (`from spatial_gym_env import ...`). + env_name="spatial_gym_env", + state_cls=SpatialGymState, + max_concurrent_envs=int(os.getenv("MAX_CONCURRENT_ENVS", "8")), +) + + +def main(): + """Entry point for `uv run --project . server` and `python -m spatial_gym_env.server.app`.""" + import argparse + + import uvicorn + + parser = argparse.ArgumentParser() + parser.add_argument("--host", default="0.0.0.0") + parser.add_argument("--port", type=int, default=8000) + args = parser.parse_args() + uvicorn.run(app, host=args.host, port=args.port) + + +if __name__ == "__main__": + main() diff --git a/envs/spatial_gym_env/server/board_svg.py b/envs/spatial_gym_env/server/board_svg.py new file mode 100644 index 000000000..1050e00c0 --- /dev/null +++ b/envs/spatial_gym_env/server/board_svg.py @@ -0,0 +1,157 @@ +"""Draw a SPaRC board as SVG for the web playground, in the colours of the SPaRC paper's figures. + +Grid positions (x, y) follow `puzzle_array`: nodes sit where x and y are even, rule cells where both +are odd, and the lanes between nodes where exactly one is odd. +""" + +import math +from html import escape +from typing import Dict, List, Optional, Sequence + +BOARD, TRACK, CELL, LINE, INK = "#132e2a", "#2e3d3b", "#00a07a", "#ffffff", "#1f2523" +COLORS = { + "K": "#1f2523", + "R": "#c8553d", + "P": "#8a63c2", + "W": "#f4f1ea", + "Y": "#ecc94b", + "B": "#3f6fa8", + "G": "#3e8e5e", + "O": "#e08a3c", +} +U = 32 # pixels per grid position (half a cell) +LANE = 0.42 * U +MARGIN = 1.3 * U +NUB = 0.62 * U + + +def _xy(x: float, y: float) -> str: + return f"{MARGIN + x * U:.1f},{MARGIN + y * U:.1f}" + + +def _symbol( + cx: float, cy: float, code: str, polyshapes: Dict[str, List[List[int]]] +) -> str: + parts = code.split("-") + kind = parts[0] + color = COLORS.get(parts[1], INK) if len(parts) > 1 else INK + px, py = MARGIN + cx * U, MARGIN + cy * U + if kind == "o": + s = 0.6 * U + return f'' + if kind == "*": + pts = [] + for i in range(16): + r = (0.36 if i % 2 == 0 else 0.19) * U + a = math.pi / 8 * i - math.pi / 2 + pts.append(f"{px + r * math.cos(a):.1f},{py + r * math.sin(a):.1f}") + return f'' + if kind in "ABCD" and len(kind) == 1: + n = "ABCD".index(kind) + 1 + w, g = 0.34 * U, 0.076 * U + x0 = px - (n * w + (n - 1) * g) / 2 + out = [] + for i in range(n): + tx = x0 + i * (w + g) + out.append( + f'' + ) + return "".join(out) + if kind in ("P", "Y") and len(parts) > 2 and parts[2] in polyshapes: + grid = polyshapes[parts[2]] + filled = [(i, j) for j, row in enumerate(grid) for i, v in enumerate(row) if v] + if not filled: + return "" + mi, ma = min(i for i, _ in filled), max(i for i, _ in filled) + mj, mb = min(j for _, j in filled), max(j for _, j in filled) + s, g = 0.21 * U, 0.048 * U + bw, bh = (ma - mi + 1) * s + (ma - mi) * g, (mb - mj + 1) * s + (mb - mj) * g + out = [] + for i, j in filled: + rx, ry = px - bw / 2 + (i - mi) * (s + g), py - bh / 2 + (j - mj) * (s + g) + if kind == "P": + out.append( + f'' + ) + else: + out.append( + f'' + ) + return "".join(out) + return "" + + +def board_svg( + board: Sequence[Sequence[str]], + path: Sequence[Sequence[int]], + polyshapes: Optional[Dict[str, List[List[int]]]] = None, + label: str = "SPaRC puzzle", +) -> str: + """SVG of the puzzle in `board` (the original puzzle_array) with the line `path` drawn on it.""" + polyshapes = polyshapes or {} + h, w = len(board), len(board[0]) + marks = {board[y][x]: (x, y) for y in range(h) for x in range(w)} + start, end = marks.get("S"), marks.get("E") + width, height = 2 * MARGIN + (w - 1) * U, 2 * MARGIN + (h - 1) * U + out = [ + f'', + f'', + ] + lanes, gaps = [], [] + for y in range(h): + for x in range(w): + if x % 2 == y % 2: + continue + a, b = ((x - 1, y), (x + 1, y)) if x % 2 else ((x, y - 1), (x, y + 1)) + lanes.append(f"M{_xy(*a)}L{_xy(*b)}") + if board[y][x] == "G": + # A gap is a cut in the lane: board colour over its middle. + along, across = 0.5 * U, LANE + 2 + gw, gh = (along, across) if x % 2 else (across, along) + gaps.append( + f'' + ) + if end is not None: + ex, ey = end + dx, dy = ( + (-1, 0) + if ex == 0 + else (1, 0) + if ex == w - 1 + else (0, -1) + if ey == 0 + else (0, 1) + ) + nub = f"{MARGIN + ex * U + dx * NUB:.1f},{MARGIN + ey * U + dy * NUB:.1f}" + lanes.append(f"M{_xy(ex, ey)}L{nub}") + out.append( + f'' + ) + out.extend(gaps) + for y in range(1, h, 2): + for x in range(1, w, 2): + out.append( + f'' + ) + out.append(_symbol(x, y, board[y][x], polyshapes)) + if len(path) > 1: + pts = "L".join(_xy(px, py) for px, py in path) + out.append( + f'' + ) + if start is not None: + cx, cy = start + out.append( + f'' + ) + for y in range(h): + for x in range(w): + if board[y][x] == ".": + cx, cy, r = MARGIN + x * U, MARGIN + y * U, 0.18 * U + pts = " ".join( + f"{cx + r * math.cos(math.pi / 3 * i + math.pi / 6):.1f},{cy + r * math.sin(math.pi / 3 * i + math.pi / 6):.1f}" + for i in range(6) + ) + out.append(f'') + out.append("") + return "".join(out) diff --git a/envs/spatial_gym_env/server/prompts.py b/envs/spatial_gym_env/server/prompts.py new file mode 100644 index 000000000..321d254e6 --- /dev/null +++ b/envs/spatial_gym_env/server/prompts.py @@ -0,0 +1,216 @@ +# The prompt strings keep the paper's exact whitespace. +# ruff: noqa: W291, W293 + +"""Prompts for the Spatial-Gym environment. + +Copied unchanged from the Spatial-Gym verifiers environment +(https://github.com/lkaesberg/Spatial-Gym/tree/main/environments/spatial_gym), which copies them from the +SPaRC reference code. So an agent gets the text the Spatial-Gym paper used, with the polyshape +and negative polyshape rules stated the way the puzzles were built (as in SPaRC 0.7.0). The move parser is in +models.py, because clients use it too. +""" + +import json +from typing import Dict + + +def _get_polyshapes_str(puzzle_data: Dict) -> str: + """Extract polyshapes string from puzzle data.""" + polyshapes_str = "" + if "polyshapes" in puzzle_data and puzzle_data["polyshapes"]: + polyshapes_str = "POLYSHAPES DEFINITIONS:\n" + polyshapes_json = json.loads(puzzle_data["polyshapes"]) + for shape_id, shape_def in polyshapes_json.items(): + shape_def_str = "\n".join(map(str, shape_def)) + polyshapes_str += f"Shape {shape_id}:\n{shape_def_str}\n\n" + return polyshapes_str + + +def step_prompt(puzzle_data: Dict) -> Dict: + """ + Generate the complete prompt dict for step-by-step gym mode (no traceback). + + Args: + puzzle_data: The puzzle data dictionary + + Returns: + Dict with 'system' and 'user' message content + """ + polyshapes_str = _get_polyshapes_str(puzzle_data) + + system_content = f""" + You are an autonomous agent controlling a path‐finding puzzle solver. + Your goal is to find a valid path (a continuous line) from the specified Start Node to the End Node on the provided grid, adhering to all puzzle rules. + + Core Concepts & Grid Basics: + Grid Dimensions: You can find the puzzle grid size in the info + Path: The solution is a single, continuous line connecting adjacent nodes either horizontally or vertically. + Revisiting: You can not traceback your path. you can not visit a cell twice. + Rule Cells: Cells containing rule symbols (squares, stars, etc.) have coordinates where both x and y are odd. + The path goes around these rule cells, never on them. They are also marked as gaps. + Regions: The drawn path divides the grid cells into one or more distinct enclosed areas (regions). Many rules apply based on the contents of these regions. + Valid Path Cells: The path travels along the grid lines (edges between nodes). It can only occupy positions marked `+` or `.` in the grid layout (these correspond to positions with at least one even coordinate). + Rule Cells: Cells containing rule symbols (squares, stars, etc.) have coordinates where both `x` and `y` are odd. The path goes *around* these rule cells, never *on* them. + Regions: The drawn path divides the grid cells into one or more distinct enclosed areas (regions). Many rules apply based on the contents of these regions. + + + Symbol Legend (Grid Notation) + * `S`: **Start Node** (Path begins here) + * `E`: **End Node** (Path ends here) + * `V`: **Visited Node** (Path has passed through this cell) + * `L`: **Current Node** (Path is currently on this cell) + * `+`: Valid cell for the path to occupy + * `N`: Empty rule cell (no rule) + * `G`: **Gap** (Path **CANNOT** cross this cell) + * `.`: **Dot** (Path **MUST** pass through this cell) + * `o-X`: **Square** of color X + * `*-X`: **Star** of color X + * `A-X`: **Triangle** (touch 1 edge) + * `B-X`: **Triangle** (touch 2 edges) + * `C-X`: **Triangle** (touch 3 edges) + * `D-X`: **Triangle** (touch 4 edges) + * `P-X-Y`: **Polyshape** (positive) of color X and shape ID Y + * `Y-X-Y`: **Negative Polyshape** (ylop) of color X and shape ID Y + + **Color Codes:** R=Red, B=Blue, G=Green, Y=Yellow, W=White, O=Orange, P=Purple, K=Black + + + Detailed Solving Rules: + The drawn path must satisfy ALL applicable constraints: + + 1. Path Constraints: + Path connects adjacent nodes (horizontal/vertical moves only). + Nodes CAN NOT be revisited. You cannot visit a cell twice. + Path MUST pass through all Dot cells. + Path CANNOT pass through any Gap cells. + + 2. Region-Based Rules (Apply to areas enclosed by the path): + Squares: All squares within a single region MUST be the same color. Squares of different colors MUST be separated into different regions by the path. + Stars: Within a single region, each star symbol MUST be paired with exactly ONE other element of the same color. Other colors within the region are irrelevant to this specific star's rule. + + Polyshapes(poly): The region containing this symbol MUST have exactly the specified shape (defined in Polyshape Definitions): the shape fills the whole region, with no cells left over. + If multiple positive polyshapes are in one region, their shapes together must fill the region exactly, without overlapping. Rotation of polyshapes is NOT allowed. They must fit in their given orientation. The color of a polyshape does not matter for this rule. + + Negative Polyshapes(ylop): These subtract area from the positive polyshapes in the same region. + The positive shapes, minus the cells covered by the negative shapes, must fill the region exactly, so the region's size is the total size of the positive shapes minus the total size of the negative shapes. + A negative shape does not have to match the shape or color of any positive shape. If the negative shapes cancel the positive shapes completely, the region has no shape constraint. + + 3. Path-Based Rules (Edge Touching): + Triangles: The path MUST touch a specific number of edges of the cell containing the triangle symbol. + (1): Path touches EXACTLY 1 edge of the triangle's cell. + (2): Path touches EXACTLY 2 edges of the triangle's cell. + (3): Path touches EXACTLY 3 edges of the triangle's cell. + (4): Path touches EXACTLY 4 edges (fully surrounds) the triangle's cell. + + Polyshape Definitions: Shapes are defined by 2D arrays where 1 indicates an occupied cell and 0 indicates an empty cell. + {polyshapes_str} + + At each turn you'll receive the current state: + - Step: The current step number + - Current Position: Your current (x, y) location + - Legal Actions: Available moves with format [digit=DIRECTION, ...] + - Grid State: The current grid showing your path progress + """ + + return { + "system": system_content, + "user": None, # User content is dynamic (observation/info/reward JSON) + } + + +def step_backtrack_prompt(puzzle_data: Dict) -> Dict: + """ + Generate the complete prompt dict for step-by-step gym mode with traceback. + + Args: + puzzle_data: The puzzle data dictionary + + Returns: + Dict with 'system' and 'user' message content + """ + polyshapes_str = _get_polyshapes_str(puzzle_data) + + system_content = f""" + You are an autonomous agent controlling a path‐finding puzzle solver. + Your goal is to find a valid path (a continuous line) from the specified Start Node to the End Node on the provided grid, adhering to all puzzle rules. + + Core Concepts & Grid Basics: + Grid Dimensions: You can find the puzzle grid size in the info + Path: The solution is a single, continuous line connecting adjacent nodes either horizontally or vertically. + Revisiting: You can traceback your path, but you MUST do so in the same way you came, without crossing over your own path. + When tracing back, you can only move to the last cell you occupied, and then continue from there. Also when you trace back, the nodes you no longer use in your path are free to be used again. + Rule Cells: Cells containing rule symbols (squares, stars, etc.) have coordinates where both x and y are odd. + The path goes around these rule cells, never on them. They are also marked as gaps. + Regions: The drawn path divides the grid cells into one or more distinct enclosed areas (regions). Many rules apply based on the contents of these regions. + Valid Path Cells: The path travels along the grid lines (edges between nodes). It can only occupy positions marked `+` or `.` in the grid layout (these correspond to positions with at least one even coordinate). + Rule Cells: Cells containing rule symbols (squares, stars, etc.) have coordinates where both `x` and `y` are odd. The path goes *around* these rule cells, never *on* them. + + + Symbol Legend (Grid Notation) + * `S`: **Start Node** (Path begins here) + * `E`: **End Node** (Path ends here) + * `V`: **Visited Node** (Path has passed through this cell) + * `L`: **Current Node** (Path is currently on this cell) + * `+`: Valid cell for the path to occupy + * `N`: Empty rule cell (no rule) + * `G`: **Gap** (Path **CANNOT** cross this cell) + * `.`: **Dot** (Path **MUST** pass through this cell) + * `o-X`: **Square** of color X + * `*-X`: **Star** of color X + * `A-X`: **Triangle** (touch 1 edge) + * `B-X`: **Triangle** (touch 2 edges) + * `C-X`: **Triangle** (touch 3 edges) + * `D-X`: **Triangle** (touch 4 edges) + * `P-X-Y`: **Polyshape** (positive) of color X and shape ID Y + * `Y-X-Y`: **Negative Polyshape** (ylop) of color X and shape ID Y + + **Color Codes:** R=Red, B=Blue, G=Green, Y=Yellow, W=White, O=Orange, P=Purple, K=Black + + + Detailed Solving Rules: + The drawn path must satisfy ALL applicable constraints: + + 1. Path Constraints: + Path connects adjacent nodes (horizontal/vertical moves only). + Nodes CAN be revisited. But only if you trace back to the last cell you occupied (and from there again and again ...). + Otherwise you CANNOT cross your own path. + Path MUST pass through all Dot cells. + Path CANNOT pass through any Gap cells. + + 2. Region-Based Rules (Apply to areas enclosed by the path): + Squares: All squares within a single region MUST be the same color. Squares of different colors MUST be separated into different regions by the path. + Stars: Within a single region, each star symbol MUST be paired with exactly ONE other element of the same color. Other colors within the region are irrelevant to this specific star's rule. + + Polyshapes(poly): The region containing this symbol MUST have exactly the specified shape (defined in Polyshape Definitions): the shape fills the whole region, with no cells left over. + If multiple positive polyshapes are in one region, their shapes together must fill the region exactly, without overlapping. Rotation of polyshapes is NOT allowed. They must fit in their given orientation. The color of a polyshape does not matter for this rule. + + Negative Polyshapes(ylop): These subtract area from the positive polyshapes in the same region. + The positive shapes, minus the cells covered by the negative shapes, must fill the region exactly, so the region's size is the total size of the positive shapes minus the total size of the negative shapes. + A negative shape does not have to match the shape or color of any positive shape. If the negative shapes cancel the positive shapes completely, the region has no shape constraint. + + 3. Path-Based Rules (Edge Touching): + Triangles: The path MUST touch a specific number of edges of the cell containing the triangle symbol. + (1): Path touches EXACTLY 1 edge of the triangle's cell. + (2): Path touches EXACTLY 2 edges of the triangle's cell. + (3): Path touches EXACTLY 3 edges of the triangle's cell. + (4): Path touches EXACTLY 4 edges (fully surrounds) the triangle's cell. + + Polyshape Definitions: Shapes are defined by 2D arrays where 1 indicates an occupied cell and 0 indicates an empty cell. + {polyshapes_str} + + At each turn you'll receive the current state: + - Step: The current step number + - Current Position: Your current (x, y) location + - Legal Actions: Available moves with format [digit=DIRECTION, ...] + - Grid State: The current grid showing your path progress + """ + + return { + "system": system_content, + "user": None, # User content is dynamic (observation/info/reward JSON) + } + + +# Per-step user message (sparc/process_puzzle.py, process_puzzle_step_by_step). +# Only change: the f-string expression {step + 1} became the placeholder {step}. +STEP_USER_TEMPLATE = "Step: {step}\nCurrent Position: {agent_loc}\nLegal Actions: [{legal_str}]\n\nGrid State:\n{obs_str}\n\nYou MAY think step-by-step, but you MUST end your response with:\nFinal: \nWhere is one of 0=RIGHT, 1=UP, 2=LEFT, 3=DOWN (you may also write the direction name, e.g. Final: right)." diff --git a/envs/spatial_gym_env/server/requirements.txt b/envs/spatial_gym_env/server/requirements.txt new file mode 100644 index 000000000..f90982a4f --- /dev/null +++ b/envs/spatial_gym_env/server/requirements.txt @@ -0,0 +1,6 @@ +openenv>=0.8.0 +Spatial-Gym>=0.1.5 +datasets>=3.6.0 +gymnasium>=0.28.1 +fastapi>=0.115.0 +uvicorn>=0.24.0 diff --git a/envs/spatial_gym_env/server/spatial_gym_environment.py b/envs/spatial_gym_env/server/spatial_gym_environment.py new file mode 100644 index 000000000..0c67fbe25 --- /dev/null +++ b/envs/spatial_gym_env/server/spatial_gym_environment.py @@ -0,0 +1,277 @@ +"""Spatial-Gym served through OpenEnv. + +Each episode is one SPaRC puzzle, solved one move per step as in the Spatial-Gym paper. +The reward is sparse: 1.0 when the episode ends with a line that exactly matches one of +the puzzle's stored solutions (`outcome_reward == 1` in `Spatial_Gym.step`), else 0.0. + +`reset()` options, all optional: + puzzle_id play this SPaRC puzzle + difficulty a level (1-5) or a list of levels to draw the puzzle from + seed makes the draw repeatable + split "test" (default, the 500 benchmark puzzles) or "train" + backtracking allow stepping back onto the previous node to undo a move (default False) + max_steps the episode ends after this many moves (default 100, as in the paper) +""" + +import copy +import json +import random +import threading +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union +from uuid import uuid4 + +import gymnasium as gym +from openenv.core.env_server.interfaces import Environment +from openenv.core.env_server.types import EnvironmentMetadata + +import Spatial_Gym # noqa: F401 (registers the "Spatial-Gym" gymnasium id) + +try: + from ..models import SpatialGymAction, SpatialGymObservation, SpatialGymState + from .board_svg import board_svg + from .prompts import STEP_USER_TEMPLATE, step_backtrack_prompt, step_prompt +except ImportError: + from models import SpatialGymAction, SpatialGymObservation, SpatialGymState + from server.board_svg import board_svg + from server.prompts import STEP_USER_TEMPLATE, step_backtrack_prompt, step_prompt + +DATASET = "lkaesberg/SPaRC" +SPLITS = ("test", "train") +ACTION_NAMES = {0: "RIGHT", 1: "UP", 2: "LEFT", 3: "DOWN"} +ARROWS = {0: "→", 1: "↑", 2: "←", 3: "↓"} +MAX_STEPS_LIMIT = 10_000 +RESET_OPTIONS = {"puzzle_id", "difficulty", "split", "backtracking", "max_steps"} +PROMPT_FIELDS = ("id", "grid_size", "puzzle_array", "polyshapes", "difficulty_level") + +# Building a Spatial-Gym env loads and preprocesses a whole split (about 2 s), and reusing +# one env across episodes leaks visited cells. So, as in the verifiers environment, we keep +# one untouched template per (split, backtracking, max_steps) and deep-copy it per episode. +# Templates are only ever copied, never stepped, so sessions share no mutable state. +_LOCK = threading.Lock() +_TEMPLATES: Dict[Tuple[str, bool, int], gym.Env] = {} +_ROWS: Dict[str, Dict[str, dict]] = {} + + +def _template(split: str, backtracking: bool, max_steps: int) -> gym.Env: + key = (split, backtracking, max_steps) + with _LOCK: + if key not in _TEMPLATES: + _TEMPLATES[key] = gym.make( + "Spatial-Gym", + df_name=DATASET, + df_split="all", + df_set=split, + render_mode=None, + observation="SPaRC", + traceback=backtracking, + max_steps=max_steps, + ) + return _TEMPLATES[key] + + +def _rows(split: str) -> Dict[str, dict]: + """The raw SPaRC records of a split, by id. The prompts read these fields.""" + with _LOCK: + if split not in _ROWS: + from datasets import load_dataset + + # Same dataset version that Spatial_Gym itself loads (no revision pin). + records = load_dataset(DATASET, "all", split=split) + _ROWS[split] = {r["id"]: {k: r[k] for k in PROMPT_FIELDS} for r in records} + return _ROWS[split] + + +def _plain(value: Any) -> Any: + """Turn numpy values in Spatial-Gym's info dicts into JSON types.""" + if isinstance(value, dict): + return {str(k): _plain(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [_plain(v) for v in value] + if hasattr(value, "tolist"): + return _plain(value.tolist()) + if hasattr(value, "item"): + return value.item() + return value + + +def _levels(difficulty: Union[int, Sequence[int], None]) -> Optional[set]: + if difficulty is None: + return None + levels = ( + {int(difficulty)} + if isinstance(difficulty, (int, float, str)) + else {int(d) for d in difficulty} + ) + if not levels or not levels <= {1, 2, 3, 4, 5}: + raise ValueError(f"difficulty must be levels from 1 to 5, got {difficulty!r}") + return levels + + +class SpatialGymEnvironment( + Environment[SpatialGymAction, SpatialGymObservation, SpatialGymState] +): + SUPPORTS_CONCURRENT_SESSIONS: bool = True + + def __init__(self): + super().__init__() + self._env: Optional[gym.Env] = None + self._row: Optional[dict] = None + self._state = SpatialGymState(episode_id=str(uuid4())) + + def reset( + self, + seed: Optional[int] = None, + episode_id: Optional[str] = None, + puzzle_id: Optional[str] = None, + difficulty: Union[int, List[int], None] = None, + split: str = "test", + backtracking: bool = False, + max_steps: int = 100, + **kwargs: Any, + ) -> SpatialGymObservation: + if kwargs: + raise ValueError( + f"unknown reset options {sorted(kwargs)}; use {sorted(RESET_OPTIONS | {'seed'})}" + ) + if split not in SPLITS: + raise ValueError(f"split must be one of {SPLITS}, got {split!r}") + max_steps = int(max_steps) + if not 1 <= max_steps <= MAX_STEPS_LIMIT: + raise ValueError( + f"max_steps must be between 1 and {MAX_STEPS_LIMIT}, got {max_steps}" + ) + backtracking = bool(backtracking) + + rows = _rows(split) + if puzzle_id is not None: + if puzzle_id not in rows: + raise ValueError(f"puzzle {puzzle_id!r} is not in the {split} split") + row = rows[puzzle_id] + else: + levels = _levels(difficulty) + candidates = sorted( + pid + for pid, r in rows.items() + if levels is None or r["difficulty_level"] in levels + ) + row = rows[random.Random(seed).choice(candidates)] + + env = copy.deepcopy(_template(split, backtracking, max_steps)) + obs, info = env.reset(options={"puzzle_id": row["id"]}) + inner = env.unwrapped + if inner.puzzles[inner.current_puzzle_index]["id"] != row["id"]: + raise RuntimeError(f"Spatial-Gym did not load puzzle {row['id']}") + self._env, self._row = env, row + self._state = SpatialGymState( + episode_id=episode_id or str(uuid4()), + step_count=0, + puzzle_id=row["id"], + difficulty=int(row["difficulty_level"]), + split=split, + backtracking=backtracking, + max_steps=max_steps, + path=self._path(), + seed=seed, + ) + system_prompt = (step_backtrack_prompt if backtracking else step_prompt)(row)[ + "system" + ] + return self._observation(obs, info, system_prompt=system_prompt) + + def step( + self, action: SpatialGymAction, timeout_s: Optional[float] = None, **kwargs: Any + ) -> SpatialGymObservation: + if self._env is None: + raise RuntimeError("call reset() before step()") + if self._state.done: + raise RuntimeError("the episode is over; call reset() to start a new one") + obs, _, terminated, truncated, info = self._env.step(int(action.action)) + self._state.step_count += 1 + self._state.path = self._path() + self._state.done = bool(terminated or truncated) + self._state.solved = ( + self._state.done and self._env.unwrapped.outcome_reward == 1 + ) + return self._observation(obs, info) + + @property + def state(self) -> SpatialGymState: + return self._state + + def get_metadata(self) -> EnvironmentMetadata: + return EnvironmentMetadata( + name="spatial_gym_env", + description=( + "SPaRC grid pathfinding puzzles, solved one move per step (Spatial-Gym). " + "Reward 1.0 when the line matches a stored solution, else 0.0." + ), + version="0.1.0", + author="Lars Benedikt Kaesberg", + documentation_url="https://github.com/openenv/OpenEnv/tree/main/envs/spatial_gym_env", + ) + + def close(self) -> None: + self._env = None + + def web_actions( + self, observation: Dict[str, Any] + ) -> List[Tuple[str, Dict[str, Any]]]: + """The legal moves as buttons, named by direction.""" + return [ + (f"{ARROWS[a]} {ACTION_NAMES[a].lower()}", {"action": a}) + for a in sorted(observation.get("legal_actions") or []) + ] + + def render_web(self, observation: Dict[str, Any]) -> Optional[str]: + """Draw this episode's puzzle with the observation's line on it.""" + if self._row is None: + return None + row = self._row + polyshapes = json.loads(row["polyshapes"]) if row["polyshapes"] else {} + label = f"SPaRC puzzle {row['id']}, level {row['difficulty_level']}" + status = f"Level {row['difficulty_level']} · step {observation.get('step', 0)}" + if observation.get("done"): + status += " · solved" if observation.get("solved") else " · not solved" + return ( + '
' + + board_svg( + row["puzzle_array"], observation.get("path") or [], polyshapes, label + ) + + f'{status}
' + ) + + def _path(self) -> List[List[int]]: + return [[int(x), int(y)] for x, y in self._env.unwrapped.path] + + def _observation( + self, obs: str, info: dict, system_prompt: str = "" + ) -> SpatialGymObservation: + inner = self._env.unwrapped + x, y = (int(v) for v in info["agent_location"]) + legal = [int(a) for a in info["legal_actions"]] + grid = json.loads(obs) + done = self._state.done + prompt = "" + if not done: + prompt = STEP_USER_TEMPLATE.format( + step=self._state.step_count + 1, + agent_loc=f"({x}, {y})", + legal_str=", ".join(f"{a}={ACTION_NAMES[a]}" for a in legal), + obs_str="\n".join(str(row) for row in grid), + ) + return SpatialGymObservation( + puzzle_id=self._state.puzzle_id, + difficulty=self._state.difficulty, + step=self._state.step_count, + position=[x, y], + path=self._path(), + legal_actions=legal, + grid=grid, + system_prompt=system_prompt, + prompt=prompt, + solved=self._state.solved, + reached_end=(x, y) == tuple(int(v) for v in inner.target_location), + done=done, + reward=1.0 if self._state.solved else 0.0, + metadata={"rule_status": _plain(info.get("rule_status", {}))}, + ) diff --git a/envs/spatial_gym_env/uv.lock b/envs/spatial_gym_env/uv.lock new file mode 100644 index 000000000..bc853db0b --- /dev/null +++ b/envs/spatial_gym_env/uv.lock @@ -0,0 +1,5210 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + 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a/tests/envs/test_spatial_gym_environment.py b/tests/envs/test_spatial_gym_environment.py new file mode 100644 index 000000000..8b70aee44 --- /dev/null +++ b/tests/envs/test_spatial_gym_environment.py @@ -0,0 +1,223 @@ +"""Tests for the Spatial-Gym environment (downloads the SPaRC dataset on first use).""" + +import json +import socket +import threading +import time +import urllib.request + +import pytest + +pytest.importorskip("Spatial_Gym", reason="Spatial-Gym is not installed") + +import uvicorn # noqa: E402 +from datasets import load_dataset # noqa: E402 +from spatial_gym_env import SpatialGymAction, SpatialGymEnv # noqa: E402 +from spatial_gym_env.models import parse_action, SpatialGymState # noqa: E402 +from spatial_gym_env.server.app import app # noqa: E402 +from spatial_gym_env.server.spatial_gym_environment import ( # noqa: E402 + SpatialGymEnvironment, +) + +MOVES = {(1, 0): 0, (0, -1): 1, (-1, 0): 2, (0, 1): 3} + + +@pytest.fixture(scope="module") +def sparc_rows(): + return list(load_dataset("lkaesberg/SPaRC", "all", split="test")) + + +def one_per_level(rows): + seen = {} + for row in rows: + seen.setdefault(row["difficulty_level"], row) + return [seen[level] for level in sorted(seen)] + + +def gold_moves(row): + path = [(p["x"], p["y"]) for p in row["solutions"][0]["path"]] + return [MOVES[(b[0] - a[0], b[1] - a[1])] for a, b in zip(path, path[1:])] + + +def test_gold_solution_solves_each_level(sparc_rows): + for row in one_per_level(sparc_rows): + env = SpatialGymEnvironment() + obs = env.reset(puzzle_id=row["id"]) + assert obs.puzzle_id == row["id"] and obs.difficulty == row["difficulty_level"] + assert obs.system_prompt and obs.prompt.startswith("Step: 1\n") + for move in gold_moves(row): + assert not obs.done + assert move in obs.legal_actions + obs = env.step(SpatialGymAction(action=move)) + assert obs.done and obs.solved and obs.reached_end + assert obs.reward == 1.0 and obs.prompt == "" + assert env.state.solved and env.state.done + assert env.state.path[-1] == obs.position + + +def test_wrong_line_scores_zero(sparc_rows): + row = one_per_level(sparc_rows)[0] + env = SpatialGymEnvironment() + obs = env.reset(puzzle_id=row["id"], max_steps=3) + illegal = next(a for a in range(4) if a not in obs.legal_actions) + start = obs.position + for _ in range(3): + obs = env.step(SpatialGymAction(action=illegal)) + # An illegal move leaves the line where it is but still counts as a step. + assert obs.position == start and obs.step == 3 + assert obs.done and not obs.solved and obs.reward == 0.0 + + +def test_backtracking_undoes_a_move(sparc_rows): + row = one_per_level(sparc_rows)[1] + env = SpatialGymEnvironment() + obs = env.reset(puzzle_id=row["id"], backtracking=True) + first = gold_moves(row)[0] + back = {0: 2, 1: 3, 2: 0, 3: 1}[first] + obs = env.step(SpatialGymAction(action=first)) + assert len(env.state.path) == 2 + obs = env.step(SpatialGymAction(action=back)) + assert len(env.state.path) == 1 and obs.position == env.state.path[0] + + +def test_reset_options(): + env = SpatialGymEnvironment() + a = env.reset(seed=7, difficulty=[4, 5]) + b = SpatialGymEnvironment().reset(seed=7, difficulty=[4, 5]) + assert a.puzzle_id == b.puzzle_id and a.difficulty in (4, 5) + train = env.reset(split="train", difficulty=2, seed=1) + assert env.state.split == "train" and train.difficulty == 2 + for bad in ( + {"split": "dev"}, + {"difficulty": 6}, + {"max_steps": 0}, + {"puzle_id": "x"}, + {"puzzle_id": "nope"}, + ): + with pytest.raises(ValueError): + env.reset(**bad) + + +def test_step_needs_an_open_episode(sparc_rows): + env = SpatialGymEnvironment() + with pytest.raises(RuntimeError): + env.step(SpatialGymAction(action=0)) + row = one_per_level(sparc_rows)[0] + env.reset(puzzle_id=row["id"]) + for move in gold_moves(row): + env.step(SpatialGymAction(action=move)) + with pytest.raises(RuntimeError): + env.step(SpatialGymAction(action=0)) + + +def test_sessions_are_independent(sparc_rows): + row = one_per_level(sparc_rows)[0] + one, two = SpatialGymEnvironment(), SpatialGymEnvironment() + one.reset(puzzle_id=row["id"]) + two.reset(puzzle_id=row["id"]) + one.step(SpatialGymAction(action=gold_moves(row)[0])) + assert len(one.state.path) == 2 and len(two.state.path) == 1 + + +def test_web_playground_draws_the_board(sparc_rows): + row = one_per_level(sparc_rows)[4] + env = SpatialGymEnvironment() + assert env.render_web({}) is None + obs = env.reset(puzzle_id=row["id"]) + buttons = env.web_actions(obs.model_dump()) + assert [action["action"] for _, action in buttons] == sorted(obs.legal_actions) + obs = env.step(SpatialGymAction(action=gold_moves(row)[0])) + assert obs.path == env.state.path and len(obs.path) == 2 + html = env.render_web(obs.model_dump()) + assert html.count("maybe 0 Final: left").action == 2 + with pytest.raises(ValueError): + SpatialGymAction.from_reply("no move here") + with pytest.raises(ValueError): + SpatialGymAction(action=4) + + +# End to end: run the FastAPI server and play through the WebSocket client. + + +@pytest.fixture(scope="module") +def base_url(): + with socket.socket() as s: + s.bind(("127.0.0.1", 0)) + port = s.getsockname()[1] + server = uvicorn.Server( + uvicorn.Config(app, host="127.0.0.1", port=port, log_level="warning") + ) + thread = threading.Thread(target=server.run, daemon=True) + thread.start() + url = f"http://127.0.0.1:{port}" + for _ in range(100): + try: + urllib.request.urlopen(f"{url}/health", timeout=1) + break + except OSError: + time.sleep(0.1) + yield url + server.should_exit = True + thread.join(timeout=10) + + +def test_play_gold_solution_over_websocket(base_url): + row = next( + r + for r in load_dataset("lkaesberg/SPaRC", "all", split="test") + if r["difficulty_level"] == 3 + ) + path = [(p["x"], p["y"]) for p in row["solutions"][0]["path"]] + with SpatialGymEnv(base_url=base_url).sync() as env: + result = env.reset(puzzle_id=row["id"]) + assert result.observation.puzzle_id == row["id"] and not result.done + assert result.observation.system_prompt + for a, b in zip(path, path[1:]): + result = env.step( + SpatialGymAction(action=MOVES[(b[0] - a[0], b[1] - a[1])]) + ) + assert result.done and result.reward == 1.0 and result.observation.solved + state = env.state() + assert state.solved and state.step_count == len(path) - 1 + + +def test_http_and_websocket_serve_spatial_state(base_url, sparc_rows): + row = one_per_level(sparc_rows)[0] + with urllib.request.urlopen(f"{base_url}/schema") as response: + properties = json.load(response)["state"]["properties"] + assert set(properties) == set(SpatialGymState.model_fields) + + # HTTP requests each get a fresh environment; only WebSocket keeps an episode. + with urllib.request.urlopen(f"{base_url}/state") as response: + http_state = json.load(response) + assert set(http_state) == set(SpatialGymState.model_fields) + assert ( + http_state == SpatialGymState(episode_id=http_state["episode_id"]).model_dump() + ) + + with SpatialGymEnv(base_url=base_url).sync() as env: + result = env.reset(seed=7, episode_id="state-contract", puzzle_id=row["id"]) + state = env.state().model_dump() + assert set(state) == set(http_state) + assert state["episode_id"] == "state-contract" + assert state["puzzle_id"] == row["id"] + assert state["difficulty"] == row["difficulty_level"] + assert state["path"] == result.observation.path + assert state["seed"] == 7 + + +def test_two_clients_get_separate_episodes(base_url): + with ( + SpatialGymEnv(base_url=base_url).sync() as one, + SpatialGymEnv(base_url=base_url).sync() as two, + ): + first = one.reset(seed=3) + two.reset(seed=3) + one.step(SpatialGymAction(action=first.observation.legal_actions[0])) + assert one.state().step_count == 1 and two.state().step_count == 0