[DRAFT, Example] Add MCTS example

ghstack-source-id: bd98430
Pull Request resolved: pytorch#2796

Author: kurtamohler

examples/trees/mcts.py

@@ -0,0 +1,219 @@
+# 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.
+
+import torch
+import torchrl
+from tensordict import TensorDict
+
+pgn_or_fen = "fen"
+
+mask_actions = False
+
+env = torchrl.envs.ChessEnv(
+    include_pgn=False,
+    include_fen=True,
+    include_hash=True,
+    include_hash_inv=True,
+    include_san=True,
+    stateful=True,
+    mask_actions=mask_actions,
+)
+
+
+def transform_reward(td):
+    if "reward" not in td:
+        return td
+    reward = td["reward"]
+    if reward == 0.5:
+        td["reward"] = 0
+    elif reward == 1 and td["turn"]:
+        td["reward"] = -td["reward"]
+    return td
+
+
+# ChessEnv sets the reward to 0.5 for a draw and 1 for a win for either player.
+# Need to transform the reward to be:
+#   white win = 1
+#   draw = 0
+#   black win = -1
+env = env.append_transform(transform_reward)
+
+forest = torchrl.data.MCTSForest()
+forest.reward_keys = env.reward_keys + ["_visits", "_reward_sum"]
+forest.done_keys = env.done_keys
+forest.action_keys = env.action_keys
+
+if mask_actions:
+    forest.observation_keys = [f"{pgn_or_fen}_hash", "turn", "action_mask"]
+else:
+    forest.observation_keys = [f"{pgn_or_fen}_hash", "turn"]
+
+C = 2.0**0.5
+
+
+def traversal_priority_UCB1(tree, root_visits):
+    subtree = tree.subtree
+    td_subtree = subtree.rollout[:, -1]["next"]
+    visits = td_subtree["_visits"]
+    reward_sum = td_subtree["_reward_sum"].clone()
+
+    # If it's black's turn, flip the reward, since black wants to
+    # optimize for the lowest reward, not highest.
+    if not subtree.rollout[0, 0]["turn"]:
+        reward_sum = -reward_sum
+
+    if tree.rollout is None:
+        parent_visits = root_visits
+    else:
+        parent_visits = tree.rollout[-1]["next", "_visits"]
+    reward_sum = reward_sum.squeeze(-1)
+    priority = (reward_sum + C * torch.sqrt(torch.log(parent_visits))) / visits
+    priority[visits == 0] = float("inf")
+    return priority
+
+
+def _traverse_MCTS_one_step(forest, tree, env, max_rollout_steps, root_visits):
+    done = False
+    td_trees_visited = []
+
+    while not done:
+        if tree.subtree is None:
+            td_tree = tree.rollout[-1]["next"].clone()
+
+            if (td_tree["_visits"] > 0 or tree.parent is None) and not td_tree["done"]:
+                actions = env.all_actions(td_tree)
+                subtrees = []
+
+                for action in actions:
+                    td = env.step(env.reset(td_tree).update(action)).update(
+                        TensorDict(
+                            {
+                                ("next", "_visits"): 0,
+                                ("next", "_reward_sum"): env.reward_spec.zeros(),
+                            }
+                        )
+                    )
+
+                    new_node = torchrl.data.Tree(
+                        rollout=td.unsqueeze(0),
+                        node_data=td["next"].select(*forest.node_map.in_keys),
+                    )
+                    subtrees.append(new_node)
+
+                # NOTE: This whole script runs about 2x faster with lazy stack
+                # versus eager stack.
+                tree.subtree = TensorDict.lazy_stack(subtrees)
+                chosen_idx = torch.randint(0, len(subtrees), ()).item()
+                rollout_state = subtrees[chosen_idx].rollout[-1]["next"]
+
+            else:
+                rollout_state = td_tree
+
+            if rollout_state["done"]:
+                rollout_reward = rollout_state["reward"]
+            else:
+                rollout = env.rollout(
+                    max_steps=max_rollout_steps,
+                    tensordict=rollout_state,
+                )
+                rollout_reward = rollout[-1]["next", "reward"]
+            done = True
+
+        else:
+            priorities = traversal_priority_UCB1(tree, root_visits)
+            chosen_idx = torch.argmax(priorities).item()
+            tree = tree.subtree[chosen_idx]
+            td_trees_visited.append(tree.rollout[-1]["next"])
+
+    for td in td_trees_visited:
+        td["_visits"] += 1
+        td["_reward_sum"] += rollout_reward
+
+
+def traverse_MCTS(forest, root, env, num_steps, max_rollout_steps):
+    """Performs Monte-Carlo tree search in an environment.
+
+    Args:
+        forest (MCTSForest): Forest of the tree to update. If the tree does not
+            exist yet, it is added.
+        root (TensorDict): The root step of the tree to update.
+        env (EnvBase): Environment to performs actions in.
+        num_steps (int): Number of iterations to traverse.
+        max_rollout_steps (int): Maximum number of steps for each rollout.
+    """
+    if root not in forest:
+        for action in env.all_actions(root):
+            td = env.step(env.reset(root.clone()).update(action)).update(
+                TensorDict(
+                    {
+                        ("next", "_visits"): 0,
+                        ("next", "_reward_sum"): env.reward_spec.zeros(),
+                    }
+                )
+            )
+            forest.extend(td.unsqueeze(0))
+
+    tree = forest.get_tree(root)
+
+    # TODO: Add this to the root node
+    root_visits = torch.tensor([0])
+
+    for _ in range(num_steps):
+        _traverse_MCTS_one_step(forest, tree, env, max_rollout_steps, root_visits)
+        root_visits += 1
+
+    return tree
+
+
+def tree_format_fn(tree):
+    td = tree.rollout[-1]["next"]
+    return [
+        td["san"],
+        td[pgn_or_fen].split("\n")[-1],
+        td["_reward_sum"].item(),
+        td["_visits"].item(),
+    ]
+
+
+def get_best_move(fen, mcts_steps, rollout_steps):
+    root = env.reset(TensorDict({"fen": fen}))
+    tree = traverse_MCTS(forest, root, env, mcts_steps, rollout_steps)
+
+    # print('------------------------------')
+    # print(tree.to_string(tree_format_fn))
+    # print('------------------------------')
+
+    moves = []
+
+    for subtree in tree.subtree:
+        san = subtree.rollout[0]["next", "san"]
+        reward_sum = subtree.rollout[-1]["next", "_reward_sum"]
+        visits = subtree.rollout[-1]["next", "_visits"]
+        value_avg = (reward_sum / visits).item()
+        if not subtree.rollout[0]["turn"]:
+            value_avg = -value_avg
+        moves.append((value_avg, san))
+
+    moves = sorted(moves, key=lambda x: -x[0])
+
+    print("------------------")
+    for value_avg, san in moves:
+        print(f" {value_avg:0.02f} {san}")
+    print("------------------")
+
+    return moves[0][1]
+
+
+# White has M1, best move Rd8#. Any other moves lose to M2 or M1.
+fen0 = "7k/6pp/7p/7K/8/8/6q1/3R4 w - - 0 1"
+assert get_best_move(fen0, 100, 10) == "Rd8#"
+
+# Black has M1, best move Qg6#. Other moves give rough equality or worse.
+fen1 = "6qk/2R4p/7K/8/8/8/8/4R3 b - - 1 1"
+assert get_best_move(fen1, 100, 10) == "Qg6#"
+
+# White has M2, best move Rxg8+. Any other move loses.
+fen2 = "2R3qk/5p1p/7K/8/8/8/5r2/2R5 w - - 0 1"
+assert get_best_move(fen2, 1000, 10) == "Rxg8+"

test/test_env.py

@@ -4157,43 +4157,60 @@ def test_env_reset_with_hash(self, stateful, include_san):
             td_check = env.reset(td.select("fen_hash"))
             assert (td_check == td).all()
 
-    @pytest.mark.parametrize("include_fen", [False, True])
-    @pytest.mark.parametrize("include_pgn", [False, True])
+    @pytest.mark.parametrize("include_fen,include_pgn", [[False, True], [True, False]])
     @pytest.mark.parametrize("stateful", [False, True])
-    @pytest.mark.parametrize("mask_actions", [False, True])
-    def test_all_actions(self, include_fen, include_pgn, stateful, mask_actions):
-        if not stateful and not include_fen and not include_pgn:
-            # pytest.skip("fen or pgn must be included if not stateful")
-            return
-
+    @pytest.mark.parametrize("include_hash", [False, True])
+    @pytest.mark.parametrize("include_san", [False, True])
+    @pytest.mark.parametrize("append_transform", [False, True])
+    #@pytest.mark.parametrize("mask_actions", [False, True])
+    @pytest.mark.parametrize("mask_actions", [False])
+    def test_all_actions(self, include_fen, include_pgn, stateful, include_hash, include_san, append_transform, mask_actions):
         env = ChessEnv(
             include_fen=include_fen,
             include_pgn=include_pgn,
+            include_san=include_san,
+            include_hash=include_hash,
+            include_hash_inv=include_hash,
             stateful=stateful,
             mask_actions=mask_actions,
         )
-        td = env.reset()
 
-        if not mask_actions:
-            with pytest.raises(RuntimeError, match="Cannot generate legal actions"):
-                env.all_actions()
-            return
+        def transform_reward(td):
+            if "reward" not in td:
+                return td
+            reward = td["reward"]
+            if reward == 0.5:
+                td["reward"] = 0
+            elif reward == 1 and td["turn"]:
+                td["reward"] = -td["reward"]
+            return td
+
+
+        # ChessEnv sets the reward to 0.5 for a draw and 1 for a win for either player.
+        # Need to transform the reward to be:
+        #   white win = 1
+        #   draw = 0
+        #   black win = -1
+        if append_transform:
+            env = env.append_transform(transform_reward)
+
+        check_env_specs(env)
+
+        td = env.reset()
 
         # Choose random actions from the output of `all_actions`
-        for _ in range(100):
-            if stateful:
-                all_actions = env.all_actions()
-            else:
+        for step_idx in range(100):
+            if step_idx % 5 == 0:
                 # Reset the the initial state first, just to make sure
                 # `all_actions` knows how to get the board state from the input.
                 env.reset()
-                all_actions = env.all_actions(td.clone())
+            all_actions = env.all_actions(td.clone())
 
             # Choose some random actions and make sure they match exactly one of
             # the actions from `all_actions`. This part is not tested when
             # `mask_actions == False`, because `rand_action` can pick illegal
             # actions in that case.
-            if mask_actions:
+            if mask_actions and step_idx % 4 == 0:
                 # TODO: Something is wrong in `ChessEnv.rand_action` which makes
                 # it fail to work properly for stateless mode. It doesn't know
                 # how to correctly reset the board state to what is given in the
@@ -4210,7 +4227,9 @@ def test_all_actions(self, include_fen, include_pgn, stateful, mask_actions):
 
             action_idx = torch.randint(0, all_actions.shape[0], ()).item()
             chosen_action = all_actions[action_idx]
-            td = env.step(td.update(chosen_action))["next"]
+            td_new = env.step(td.update(chosen_action).clone())
+            assert (td == td_new.exclude('next')).all()
+            td = td_new['next']
 
             if td["done"]:
                 td = env.reset()

torchrl/data/map/tree.py

@@ -1364,6 +1364,11 @@ def valid_paths(cls, tree: Tree):
     def __len__(self):
         return len(self.data_map)
 
+    def __contains__(self, root: TensorDictBase):
+        if self.node_map is None:
+            return False
+        return root.select(*self.node_map.in_keys) in self.node_map
+
     def to_string(self, td_root, node_format_fn=lambda tree: tree.node_data.to_dict()):
         """Generates a string representation of a tree in the forest.