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Support nested observation structures in MCTS agent #351

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Support nested observation structures in MCTS agent #351

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5 changes: 4 additions & 1 deletion acme/agents/tf/mcts/acting.py
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Expand Up @@ -22,6 +22,7 @@
from acme.agents.tf.mcts import models
from acme.agents.tf.mcts import search
from acme.agents.tf.mcts import types
from acme.tf import utils as tf2_utils
from acme.tf import variable_utils as tf2_variable_utils

import dm_env
Expand Down Expand Up @@ -66,7 +67,9 @@ def __init__(
def _forward(
self, observation: types.Observation) -> Tuple[types.Probs, types.Value]:
"""Performs a forward pass of the policy-value network."""
logits, value = self._network(tf.expand_dims(observation, axis=0))
# Use tree.map_structure to support nested observation structures.
batched_observation = tf2_utils.add_batch_dim(observation)
logits, value = self._network(batched_observation)

# Convert to numpy & take softmax.
logits = logits.numpy().squeeze(axis=0)
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7 changes: 4 additions & 3 deletions acme/agents/tf/mcts/types.py
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Expand Up @@ -14,16 +14,17 @@

"""Type aliases and assumptions that are specific to the MCTS agent."""

from typing import Callable, Tuple, Union
from typing import Any, Callable, Tuple, Union
import numpy as np

# pylint: disable=invalid-name

# Assumption: actions are scalar and discrete (integral).
Action = Union[int, np.int32, np.int64]

# Assumption: observations are array-like.
Observation = np.ndarray
# Observations can be array-like or nested structures (e.g., dicts, tuples).
# This allows MCTS to work with environments that have complex observation spaces.
Observation = Any

# Assumption: rewards and discounts are scalar.
Reward = Union[float, np.float32, np.float64]
Expand Down