[Feature] ChessEnv

torchrl/envs/__init__.py

@@ -5,7 +5,7 @@
 
 from .batched_envs import ParallelEnv, SerialEnv
 from .common import EnvBase, EnvMetaData, make_tensordict
-from .custom import LLMHashingEnv, PendulumEnv, TicTacToeEnv
+from .custom import ChessEnv, LLMHashingEnv, PendulumEnv, TicTacToeEnv
 from .env_creator import env_creator, EnvCreator, get_env_metadata
 from .gym_like import default_info_dict_reader, GymLikeEnv
 from .libs import (

torchrl/envs/custom/__init__.py

@@ -3,6 +3,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from .chess import ChessEnv
 from .llm import LLMHashingEnv
 from .pendulum import PendulumEnv
 from .tictactoeenv import TicTacToeEnv

torchrl/envs/custom/chess.py

@@ -0,0 +1,197 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from typing import Optional
+
+import torch
+from tensordict import TensorDict, TensorDictBase
+from torchrl.data import Categorical, Composite, NonTensor, Unbounded
+
+from torchrl.envs import EnvBase
+
+from torchrl.envs.utils import _classproperty
+
+
+class ChessEnv(EnvBase):
+    """A chess environment that follows the TorchRL API.
+
+    Requires: the `chess` library. More info `here <https://python-chess.readthedocs.io/en/latest/>`__.
+
+    Args:
+        stateful (bool): Whether to keep track of the internal state of the board.
+            If False, the state will be stored in the observation and passed back
+            to the environment on each call. Default: ``False``.
+
+    .. note:: the action spec is a :class:`~torchrl.data.Categorical` spec with a ``-1`` shape.
+        Unless :meth:`~torchrl.data.Categorical.set_provisional_n` is called with the cardinality of the legal moves,
+        valid random actions cannot be taken. :meth:`~torchrl.envs.EnvBase.rand_action` has been adapted to account for
+        this behavior.
+
+    Examples:
+        >>> env = ChessEnv()
+        >>> r = env.reset()
+        >>> env.rand_step(r)
+        TensorDict(
+            fields={
+                action: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
+                done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
+                fen: NonTensorData(data=rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1, batch_size=torch.Size([]), device=None),
+                hashing: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
+                next: TensorDict(
+                    fields={
+                        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
+                        fen: NonTensorData(data=rnbqkbnr/pppppppp/8/8/8/2N5/PPPPPPPP/R1BQKBNR b KQkq - 1 1, batch_size=torch.Size([]), device=None),
+                        hashing: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
+                        reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int32, is_shared=False),
+                        terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
+                        turn: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.bool, is_shared=False)},
+                    batch_size=torch.Size([]),
+                    device=None,
+                    is_shared=False),
+                terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
+                turn: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.bool, is_shared=False)},
+            batch_size=torch.Size([]),
+            device=None,
+            is_shared=False)
+        >>> env.rollout(1000)
+        TensorDict(
+            fields={
+                action: Tensor(shape=torch.Size([322]), device=cpu, dtype=torch.int64, is_shared=False),
+                done: Tensor(shape=torch.Size([322, 1]), device=cpu, dtype=torch.bool, is_shared=False),
+                fen: NonTensorStack(
+                    ['rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQ...,
+                    batch_size=torch.Size([322]),
+                    device=None),
+                hashing: Tensor(shape=torch.Size([322]), device=cpu, dtype=torch.int64, is_shared=False),
+                next: TensorDict(
+                    fields={
+                        done: Tensor(shape=torch.Size([322, 1]), device=cpu, dtype=torch.bool, is_shared=False),
+                        fen: NonTensorStack(
+                            ['rnbqkbnr/pppppppp/8/8/2P5/8/PP1PPPPP/RNBQKBNR b ...,
+                            batch_size=torch.Size([322]),
+                            device=None),
+                        hashing: Tensor(shape=torch.Size([322]), device=cpu, dtype=torch.int64, is_shared=False),
+                        reward: Tensor(shape=torch.Size([322, 1]), device=cpu, dtype=torch.int32, is_shared=False),
+                        terminated: Tensor(shape=torch.Size([322, 1]), device=cpu, dtype=torch.bool, is_shared=False),
+                        turn: Tensor(shape=torch.Size([322]), device=cpu, dtype=torch.bool, is_shared=False)},
+                    batch_size=torch.Size([322]),
+                    device=None,
+                    is_shared=False),
+                terminated: Tensor(shape=torch.Size([322, 1]), device=cpu, dtype=torch.bool, is_shared=False),
+                turn: Tensor(shape=torch.Size([322]), device=cpu, dtype=torch.bool, is_shared=False)},
+            batch_size=torch.Size([322]),
+            device=None,
+            is_shared=False)
+
+
+    """
+
+    _hash_table = {}
+
+    @_classproperty
+    def lib(cls):
+        try:
+            import chess
+        except ImportError:
+            raise ImportError(
+                "The `chess` library could not be found. Make sure you installed it through `pip install chess`."
+            )
+        return chess
+
+    def __init__(self, stateful: bool = False):
+        chess = self.lib
+        super().__init__()
+        self.full_observation_spec = Composite(
+            hashing=Unbounded(shape=(), dtype=torch.int64),
+            fen=NonTensor(shape=()),
+            turn=Categorical(n=2, dtype=torch.bool, shape=()),
+        )
+        self.stateful = stateful
+        if not self.stateful:
+            self.full_state_spec = self.full_observation_spec.clone()
+        self.full_action_spec = Composite(
+            action=Categorical(n=-1, shape=(), dtype=torch.int64)
+        )
+        self.full_reward_spec = Composite(
+            reward=Unbounded(shape=(1,), dtype=torch.int32)
+        )
+        # done spec generated automatically
+        self.board = chess.Board()
+        if self.stateful:
+            self.action_spec.set_provisional_n(len(list(self.board.legal_moves)))
+
+    def rand_action(self, tensordict: Optional[TensorDictBase] = None):
+        self._set_action_space(tensordict)
+        return super().rand_action(tensordict)
+
+    def _reset(self, tensordict=None):
+        fen = None
+        if tensordict is not None:
+            fen = self._get_fen(tensordict)
+            dest = tensordict.empty()
+        else:
+            dest = TensorDict()
+
+        if fen is None:
+            self.board.reset()
+            fen = self.board.fen()
+        else:
+            self.board.set_fen(fen.data)
+
+        hashing = hash(fen)
+
+        self._set_action_space()
+        turn = self.board.turn
+        return dest.set("fen", fen).set("hashing", hashing).set("turn", turn)
+
+    def _set_action_space(self, tensordict: TensorDict | None = None):
+        if not self.stateful and tensordict is not None:
+            fen = self._get_fen(tensordict).data
+            self.board.set_fen(fen)
+        self.action_spec.set_provisional_n(self.board.legal_moves.count())
+
+    @classmethod
+    def _get_fen(cls, tensordict):
+        fen = tensordict.get("fen", None)
+        if fen is None:
+            hashing = tensordict.get("hashing", None)
+            if hashing is not None:
+                fen = cls._hash_table.get(hashing.item())
+        return fen
+
+    def _step(self, tensordict):
+        # action
+        action = tensordict.get("action")
+        board = self.board
+        if not self.stateful:
+            fen = self._get_fen(tensordict).data
+            board.set_fen(fen)
+        action = str(list(board.legal_moves)[action])
+        # assert chess.Move.from_uci(action) in board.legal_moves
+        board.push_san(action)
+        self._set_action_space()
+
+        # Collect data
+        fen = self.board.fen()
+        dest = tensordict.empty()
+        hashing = hash(fen)
+        dest.set("fen", fen)
+        dest.set("hashing", hashing)
+
+        done = board.is_checkmate()
+        turn = torch.tensor(board.turn)
+        reward = torch.tensor([done]).int() * (turn.int() * 2 - 1)
+        done = done | board.is_stalemate() | board.is_game_over()
+        dest.set("reward", reward)
+        dest.set("turn", turn)
+        dest.set("done", [done])
+        dest.set("terminated", [done])
+        return dest
+
+    def _set_seed(self, *args, **kwargs):
+        ...
+
+    def cardinality(self, tensordict: TensorDictBase|None=None) -> int:
+        self._set_action_space(tensordict)
+        return self.action_spec.cardinality()