amend

Author: Vincent Moens

sota-implementations/MCTS/AlphaZero/mcts_node.py

@@ -19,7 +19,7 @@
 
 from torchrl.objectives.value.functional import reward2go
 
-from .mcts_node import MCTSNode
+from torchrl.data import MCTSNode, MCTSChildren
 
 
 @dataclass
@@ -64,17 +64,17 @@ def forward(self, node: MCTSNode) -> TensorDictBase:
 
         if exploration_type() == ExplorationType.RANDOM or exploration_type() is None:
             tensordict[self.action_key] = self.explore_action(node)
-        elif exploration_type() == ExplorationType.MODE:
+        elif exploration_type() in (ExplorationType.MODE, ExplorationType.DETERMINISTIC, ExplorationType.MEAN):
             tensordict[self.action_key] = self.get_greedy_action(node)
 
         return tensordict
 
     def get_greedy_action(self, node: MCTSNode) -> torch.Tensor:
-        action = torch.argmax(node.children_visits)
+        action = torch.argmax(node.children.visits)
         return action
 
     def explore_action(self, node: MCTSNode) -> torch.Tensor:
-        action_scores = node.scores
+        action_scores = node.score
 
         max_value = torch.max(action_scores)
         action = torch.argmax(
@@ -156,9 +156,6 @@ class ExpansionStrategy:
     This policy will use to initialize a node when it gets expanded at the first time.
     """
 
-    def __init__(self):
-        super().__init__()
-
     def forward(self, node: MCTSNode) -> MCTSNode:
         """The node to be expanded.
 
@@ -179,7 +176,7 @@ def forward(self, node: MCTSNode) -> MCTSNode:
 
     @abstractmethod
     def expand(self, node: MCTSNode) -> None:
-        pass
+        ...
 
     def set_node(self, node: MCTSNode) -> None:
         self.node = node
@@ -189,7 +186,7 @@ class BatchedRootExpansionStrategy(ExpansionStrategy):
     def __init__(
         self,
         policy_module: TensorDictModule,
-        module_action_value_key: str = "action_value",
+        module_action_value_key: NestedKey = "action_value",
     ):
         super().__init__()
         assert module_action_value_key in policy_module.out_keys
@@ -200,17 +197,15 @@ def expand(self, node: MCTSNode) -> None:
         policy_netword_td = node.state.select(*self.policy_module.in_keys)
         policy_netword_td = self.policy_module(policy_netword_td)
         p_sa = policy_netword_td[self.action_value_key]
-        node.children_priors = p_sa  # prior_action_value
-        node.children_values = torch.zeros_like(p_sa)  # action_value
-        node.children_visits = torch.zeros_like(p_sa)  # action_count
+        node.children = MCTSChildren.init_from_prob(p_sa)
         # setattr(node, "truncated", torch.ones(1, dtype=torch.bool))
 
 
 class AlphaZeroExpansionStrategy(ExpansionStrategy):
     def __init__(
         self,
         policy_module: TensorDictModule,
-        module_action_value_key: str = "action_value",
+        module_action_value_key: NestedKey = "action_value",
     ):
         super().__init__()
         assert module_action_value_key in policy_module.out_keys
@@ -221,9 +216,9 @@ def expand(self, node: MCTSNode) -> None:
         policy_netword_td = node.state.select(*self.policy_module.in_keys)
         policy_netword_td = self.policy_module(policy_netword_td)
         p_sa = policy_netword_td[self.action_value_key]
-        node.children_priors = p_sa  # prior_action_value
-        node.children_values = torch.zeros_like(p_sa)  # action_value
-        node.children_visits = torch.zeros_like(p_sa)  # action_count
+        node.children.priors = p_sa  # prior_action_value
+        node.children.vals = torch.zeros_like(p_sa)  # action_value
+        node.children.visits = torch.zeros_like(p_sa)  # action_count
         # setattr(node, "truncated", torch.ones(1, dtype=torch.bool))
 
 
@@ -244,15 +239,15 @@ def __init__(
         self.node: MCTSNode
 
     def forward(self, node: MCTSNode) -> MCTSNode:
-        n = torch.sum(node.children_visits, dim=-1) + 1
+        n = torch.sum(node.children.visits, dim=-1) + 1
         u_sa = (
             self.cpuct
-            * node.children_priors
+            * node.children.priors
             * torch.sqrt(n)
-            / (1 + node.children_visits)
+            / (1 + node.children.visits)
         )
 
-        optimism_estimation = node.children_values + u_sa
+        optimism_estimation = node.children.vals + u_sa
         node.scores = optimism_estimation
 
         return node
@@ -270,17 +265,17 @@ def __init__(
         self.epsilon = epsilon
 
     def forward(self, node: MCTSNode) -> MCTSNode:
-        if node.children_priors.device.type == "mps":
-            device = node.children_priors.device
+        if node.children.priors.device.type == "mps":
+            device = node.children.priors.device
             noise = _Dirichlet.apply(
-                self.alpha * torch.ones_like(node.children_priors).cpu()
+                self.alpha * torch.ones_like(node.children.priors).cpu()
             )
             noise = noise.to(device)  # type: ignore
         else:
-            noise = _Dirichlet.apply(self.alpha * torch.ones_like(node.children_priors))
+            noise = _Dirichlet.apply(self.alpha * torch.ones_like(node.children.priors))
 
-        noisy_priors = (1 - self.epsilon) * node.children_priors + self.epsilon * noise  # type: ignore
-        node.children_priors = noisy_priors
+        noisy_priors = (1 - self.epsilon) * node.children.priors + self.epsilon * noise  # type: ignore
+        node.children.priors = noisy_priors
         return node
 
 
@@ -293,6 +288,8 @@ class MCTSPolicy(TensorDictModuleBase):
         exploration_strategy: a policy to exploration vs exploitation
     """
 
+    node: MCTSNode
+
     def __init__(
         self,
         expansion_strategy: AlphaZeroExpansionStrategy,
@@ -313,10 +310,11 @@ def __init__(
         self.expansion_strategy = expansion_strategy
         self.selection_strategy = selection_strategy
         self.exploration_strategy = exploration_strategy
-        self.node: MCTSNode
         self.batch_size = batch_size
 
     def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
+        if not hasattr(self, "node"):
+            raise RuntimeError("the MCTS policy has not been initialized. Please provide a node through policy.set_node().")
         if not self.node.expanded:
             self.node.state = tensordict  # type: ignore
         self.expansion_strategy.forward(self.node)

sota-implementations/MCTS/AlphaZero/mcts_policy.py

@@ -74,3 +74,4 @@
     UnboundedDiscreteTensorSpec,
 )
 from .utils import check_no_exclusive_keys, consolidate_spec, contains_lazy_spec
+from .mcts import MCTSNode, MCTSChildren

torchrl/data/__init__.py

@@ -0,0 +1,6 @@
+# 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 .nodes import MCTSNode, MCTSChildren

torchrl/data/mcts/__init__.py

@@ -0,0 +1,112 @@
+# 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
+
+import torch
+from tensordict import tensorclass, TensorDict
+
+@tensorclass(autocast=True)
+class MCTSChildren:
+    vals: torch.Tensor
+    priors: torch.Tensor
+    visits: torch.Tensor
+    ids: torch.Tensor | None = None
+    nodes: MCTSNode | None = None
+
+    @classmethod
+    def init_from_prob(cls, probs):
+        vals = torch.zeros_like(probs)
+        visits = torch.zeros_like(probs)
+        return cls(vals=vals, priors=probs, visits=visits)
+
+
+@tensorclass(autocast=True)
+class MCTSNode:
+    prior_action: torch.Tensor
+    _children: MCTSChildren | None = None
+    score: torch.Tensor | None = None
+    state: TensorDict | None = None
+    terminated: torch.Tensor | None = None
+    parent: MCTSNode | None = None
+
+    @classmethod
+    def from_action(
+        cls,
+        action: torch.Tensor,
+        parent: MCTSNode | None,
+    ):
+        return cls(prior_action=action, parent=parent)
+
+    @property
+    def children(self) -> MCTSChildren:
+        children = self._children
+        if children is None:
+            return MCTSChildren(*[torch.zeros((), device=self.device) for _ in range(4)])
+        return children
+
+    @children.setter
+    def children(self, value):
+        self._children = value
+
+    @property
+    def visits(self) -> torch.Tensor:
+        assert self.parent is not None
+        return self.parent.children.visits[self.prior_action]
+
+    @visits.setter
+    def visits(self, x) -> None:
+        assert self.parent is not None
+        self.parent.children.visits[self.prior_action] = x
+
+    @property
+    def value(self) -> torch.Tensor:
+        assert self.parent is not None
+        return self.parent.children.vals[self.prior_action]
+
+    @value.setter
+    def value(self, x) -> None:
+        assert self.parent is not None
+        self.parent.children.vals[self.prior_action] = x
+
+    @property
+    def expanded(self) -> bool:
+        return self.children.ids.numel() > 0
+
+    def get_child(self, action: torch.Tensor) -> MCTSNode:
+        idx = (self.children.ids == action).all(-1)
+        return self.children.nodes[idx]  # type: ignore
+
+    @classmethod
+    def root(cls) -> MCTSNode:
+        return cls(torch.Tensor(-1), None)
+
+    @classmethod
+    def dummy(cls):
+        """Creates a 'dummy' MCTSNode that can be used to explore TorchRL's MCTS API."""
+        children_values = stuff
+        children_priors = stuff
+        children_visits = stuff
+        children_ids = stuff
+        children_nodes = stuff
+        children = MCTSChildren(
+            values = children_values,
+            priors = children_priors,
+            visits = children_visits,
+            ids = children_ids,
+            nodes = children_nodes,
+        )
+        prior_action = stuff
+        score = stuff
+        state = stuff
+        terminated = stuff
+        parent = None
+        return cls(
+            prior_action=prior_action,
+            children=children,
+            score=score,
+            state=state,
+            terminated=terminated,
+            parent=parent,
+        )

torchrl/data/mcts/nodes.py

@@ -0,0 +1,7 @@
+# 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
+