Source code for torchrl.envs.custom.tictactoeenv
# 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 __future__ import annotations
from typing import Optional
import torch
from tensordict import TensorDict, TensorDictBase
from torchrl.data.tensor_specs import Categorical, Composite, Unbounded
from torchrl.envs.common import EnvBase
[docs]class TicTacToeEnv(EnvBase):
"""A Tic-Tac-Toe implementation.
Keyword Args:
single_player (bool, optional): whether one or two players have to be
accounted for. ``single_player=True`` means that ``"player1"`` is
playing randomly. If ``False`` (default), at each turn,
one of the two players has to play.
device (torch.device, optional): the device where to put the tensors.
Defaults to ``None`` (default device).
The environment is stateless. To run it across multiple batches, call
>>> env.reset(TensorDict(batch_size=desired_batch_size))
If the ``"mask"`` entry is present, ``rand_action`` takes it into account to
generate the next action. Any policy executed on this env should take this
mask into account, as well as the turn of the player (stored in the ``"turn"``
output entry).
Specs:
>>> print(env.specs)
Composite(
output_spec: Composite(
full_observation_spec: Composite(
board: Categorical(
shape=torch.Size([3, 3]),
space=DiscreteBox(n=2),
dtype=torch.int32,
domain=discrete),
turn: Categorical(
shape=torch.Size([1]),
space=DiscreteBox(n=2),
dtype=torch.int32,
domain=discrete),
mask: Categorical(
shape=torch.Size([9]),
space=DiscreteBox(n=2),
dtype=torch.bool,
domain=discrete),
shape=torch.Size([])),
full_reward_spec: Composite(
player0: Composite(
reward: UnboundedContinuous(
shape=torch.Size([1]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)),
dtype=torch.float32,
domain=continuous),
shape=torch.Size([])),
player1: Composite(
reward: UnboundedContinuous(
shape=torch.Size([1]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)),
dtype=torch.float32,
domain=continuous),
shape=torch.Size([])),
shape=torch.Size([])),
full_done_spec: Composite(
done: Categorical(
shape=torch.Size([1]),
space=DiscreteBox(n=2),
dtype=torch.bool,
domain=discrete),
terminated: Categorical(
shape=torch.Size([1]),
space=DiscreteBox(n=2),
dtype=torch.bool,
domain=discrete),
truncated: Categorical(
shape=torch.Size([1]),
space=DiscreteBox(n=2),
dtype=torch.bool,
domain=discrete),
shape=torch.Size([])),
shape=torch.Size([])),
input_spec: Composite(
full_state_spec: Composite(
board: Categorical(
shape=torch.Size([3, 3]),
space=DiscreteBox(n=2),
dtype=torch.int32,
domain=discrete),
turn: Categorical(
shape=torch.Size([1]),
space=DiscreteBox(n=2),
dtype=torch.int32,
domain=discrete),
mask: Categorical(
shape=torch.Size([9]),
space=DiscreteBox(n=2),
dtype=torch.bool,
domain=discrete), shape=torch.Size([])),
full_action_spec: Composite(
action: Categorical(
shape=torch.Size([1]),
space=DiscreteBox(n=9),
dtype=torch.int64,
domain=discrete),
shape=torch.Size([])),
shape=torch.Size([])),
shape=torch.Size([]))
To run a dummy rollout, execute the following command:
Examples:
>>> env = TicTacToeEnv()
>>> env.rollout(10)
TensorDict(
fields={
action: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.int64, is_shared=False),
board: Tensor(shape=torch.Size([9, 3, 3]), device=cpu, dtype=torch.int32, is_shared=False),
done: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.bool, is_shared=False),
mask: Tensor(shape=torch.Size([9, 9]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
board: Tensor(shape=torch.Size([9, 3, 3]), device=cpu, dtype=torch.int32, is_shared=False),
done: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.bool, is_shared=False),
mask: Tensor(shape=torch.Size([9, 9]), device=cpu, dtype=torch.bool, is_shared=False),
player0: TensorDict(
fields={
reward: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([9]),
device=None,
is_shared=False),
player1: TensorDict(
fields={
reward: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([9]),
device=None,
is_shared=False),
terminated: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.bool, is_shared=False),
turn: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.int32, is_shared=False)},
batch_size=torch.Size([9]),
device=None,
is_shared=False),
terminated: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.bool, is_shared=False),
turn: Tensor(shape=torch.Size([9, 1]), device=cpu, dtype=torch.int32, is_shared=False)},
batch_size=torch.Size([9]),
device=None,
is_shared=False)
"""
# batch_locked is set to False since various batch sizes can be provided to the env
batch_locked: bool = False
def __init__(self, *, single_player: bool = False, device=None):
super().__init__(device=device)
self.single_player = single_player
self.action_spec: Unbounded = Categorical(
n=9,
shape=(),
device=device,
)
self.full_observation_spec: Composite = Composite(
board=Unbounded(shape=(3, 3), dtype=torch.int, device=device),
turn=Categorical(
2,
shape=(1,),
dtype=torch.int,
device=device,
),
mask=Categorical(
2,
shape=(9,),
dtype=torch.bool,
device=device,
),
device=device,
)
self.state_spec: Composite = self.observation_spec.clone()
self.reward_spec: Unbounded = Composite(
{
("player0", "reward"): Unbounded(shape=(1,), device=device),
("player1", "reward"): Unbounded(shape=(1,), device=device),
},
device=device,
)
self.full_done_spec: Categorical = Composite(
done=Categorical(2, shape=(1,), dtype=torch.bool, device=device),
device=device,
)
self.full_done_spec["terminated"] = self.full_done_spec["done"].clone()
self.full_done_spec["truncated"] = self.full_done_spec["done"].clone()
def _reset(self, reset_td: TensorDict) -> TensorDict:
shape = reset_td.shape if reset_td is not None else ()
state = self.state_spec.zero(shape)
state["board"] -= 1
state["mask"].fill_(True)
return state.update(self.full_done_spec.zero(shape))
def _step(self, state: TensorDict) -> TensorDict:
board = state["board"].clone()
turn = state["turn"].clone()
action = state["action"]
board.flatten(-2, -1).scatter_(index=action.unsqueeze(-1), dim=-1, value=1)
wins = self.win(board, action)
mask = board.flatten(-2, -1) == -1
done = wins | ~mask.any(-1, keepdim=True)
terminated = done.clone()
reward_0 = wins & (turn == 0)
reward_1 = wins & (turn == 1)
state = TensorDict(
{
"done": done,
"terminated": terminated,
("player0", "reward"): reward_0.float(),
("player1", "reward"): reward_1.float(),
"board": torch.where(board == -1, board, 1 - board),
"turn": 1 - turn,
"mask": mask,
},
batch_size=state.batch_size,
)
if self.single_player:
select = (~done & (turn == 0)).squeeze(-1)
if select.all():
state_select = state
elif select.any():
state_select = state[select]
else:
return state
state_select = self._step(self.rand_action(state_select))
if select.all():
return state_select
return torch.where(done, state, state_select)
return state
def _set_seed(self, seed: int | None):
...
@staticmethod
def win(board: torch.Tensor, action: torch.Tensor):
row = action // 3 # type: ignore
col = action % 3 # type: ignore
if board[..., row, :].sum() == 3:
return True
if board[..., col].sum() == 3:
return True
if board.diagonal(0, -2, -1).sum() == 3:
return True
if board.flip(-1).diagonal(0, -2, -1).sum() == 3:
return True
return False
@staticmethod
def full(board: torch.Tensor) -> bool:
return torch.sym_int(board.abs().sum()) == 9
@staticmethod
def get_action_mask():
pass
[docs] def rand_action(self, tensordict: Optional[TensorDictBase] = None):
mask = tensordict.get("mask")
action_spec = self.action_spec
if tensordict.ndim:
action_spec = action_spec.expand(tensordict.shape)
else:
action_spec = action_spec.clone()
action_spec.update_mask(mask)
tensordict.set(self.action_key, action_spec.rand())
return tensordict
