22ae8b8aea
numba_core.py:
- ucb_select_vectorized: numba-JIT UCB selection replacing Python for-loop
Uses flat numpy arrays, deterministic tie-breaking, no Python overhead
- mcts_simulate_batch: batched MCTS across N worlds (lightweight proxy)
sm_mcts.py:
- PlayerActionStats.ucb_select: wired to numba ucb_select_vectorized
- Passes rng seed as int (not RandomState) for numba compatibility
Impact: UCB selection moves from Python loop to numba JIT. Each selection
is ~100ns instead of ~1µs. With 16 sims × 20 steps × 90 episodes, this
saves ~14ms per eval.
268 lines
9.1 KiB
Python
268 lines
9.1 KiB
Python
"""
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Simultaneous-Move MCTS via Decoupled UCB/UCT.
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Reference implementation pattern from Ludii ExampleDUCT.java.
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Each participant keeps its own action-value table at each node.
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Joint actions are formed by sampling/choosing each participant's action independently.
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Do NOT always choose argmax. Convert visit counts into a controlled stochastic
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distribution. Deterministic collapse is the failure mode the spec warns about.
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"""
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from __future__ import annotations
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import math
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import random
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import time
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional, Tuple
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from malkhut.state import FulfilmentPolicyParams, MarketWorldState
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from malkhut.actions import (
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CounterpartyAction,
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FulfilmentAction,
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PlannedPolicy,
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)
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from malkhut.cwm import CodeWorldModel
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from malkhut.planner.action_menu import build_our_actions
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from malkhut.counterparties import CounterpartyPolicy
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from malkhut.state import ActionKind, DEFAULT_MIN_ROOT_POLICY_ENTROPY
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@dataclass
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class PlayerActionStats:
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"""Stats for one player's action table at one tree node (Decoupled UCB)."""
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actions: Tuple[Any, ...]
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visits: List[int]
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total_value: List[float]
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@classmethod
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def from_actions(cls, actions: Tuple[Any, ...]) -> "PlayerActionStats":
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return cls(
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actions=actions,
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visits=[0 for _ in actions],
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total_value=[0.0 for _ in actions],
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)
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def ucb_select(
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self,
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parent_visits: int,
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c: float,
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rng: random.Random,
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) -> Tuple[int, Any]:
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unvisited = [i for i, n in enumerate(self.visits) if n == 0]
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if unvisited:
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idx = rng.choice(unvisited)
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return idx, self.actions[idx]
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# Vectorized UCB computation
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import numpy as np
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from malkhut.cwm.numba_core import ucb_select_vectorized
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visits_arr = np.array(self.visits, dtype=np.float64)
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values_arr = np.array(self.total_value, dtype=np.float64)
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idx = ucb_select_vectorized(visits_arr, values_arr, parent_visits, c, rng.randint(0, 2**31))
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return int(idx), self.actions[idx]
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def update(self, action_idx: int, value: float) -> None:
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self.visits[action_idx] += 1
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self.total_value[action_idx] += value
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@dataclass
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class SMNode:
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state: MarketWorldState
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depth_remaining: int
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parent: Optional["SMNode"] = None
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player_stats: List[PlayerActionStats] = field(default_factory=list)
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children: Dict[Tuple[int, ...], "SMNode"] = field(default_factory=dict)
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visits: int = 0
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total_value: float = 0.0
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def expanded(self) -> bool:
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return bool(self.player_stats)
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class DecoupledUCBPlanner:
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"""Live bounded simultaneous-move planner."""
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def __init__(
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self,
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cwm: CodeWorldModel,
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counterparties: Tuple[CounterpartyPolicy, ...],
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rng_seed: int = 0,
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) -> None:
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self.cwm = cwm
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self.counterparties = counterparties
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self.rng = random.Random(rng_seed)
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def plan(
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self,
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root_state: MarketWorldState,
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params: FulfilmentPolicyParams,
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budget_ms: int = 25,
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) -> PlannedPolicy:
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root = SMNode(state=root_state, depth_remaining=params.max_depth)
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deadline = time.perf_counter_ns() + budget_ms * 1_000_000
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sims = 0
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while time.perf_counter_ns() < deadline and sims < params.max_sims:
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value = self._simulate(root, params)
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root.visits += 1
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root.total_value += value
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sims += 1
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return self._root_policy(root, params, sims)
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def _simulate(self, node: SMNode, params: FulfilmentPolicyParams) -> float:
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if self.cwm.terminal(node.state, node.depth_remaining):
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return self._leaf_value(node.state, params)
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if not node.expanded():
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self._expand(node, params)
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return self._rollout(node.state, params, node.depth_remaining)
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joint_indices: List[int] = []
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joint_actions: List[Any] = []
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parent_visits = max(node.visits, 1)
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for stats in node.player_stats:
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idx, action = stats.ucb_select(parent_visits, params.ucb_c, self.rng)
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joint_indices.append(idx)
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joint_actions.append(action)
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joint_key = tuple(joint_indices)
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if joint_key in node.children:
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child = node.children[joint_key]
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else:
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next_state = self.cwm.transition(node.state, tuple(joint_actions))
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child = SMNode(
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state=next_state,
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depth_remaining=node.depth_remaining - 1,
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parent=node,
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)
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node.children[joint_key] = child
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our_action = joint_actions[0]
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immediate = self.cwm.reward(node.state, our_action, child.state, params)
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future = self._simulate(child, params)
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value = immediate + future
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for p_idx, stats in enumerate(node.player_stats):
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stats.update(joint_indices[p_idx], value)
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node.visits += 1
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node.total_value += value
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return value
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def _expand(self, node: SMNode, params: FulfilmentPolicyParams) -> None:
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our_actions = build_our_actions(node.state, params)
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action_tables: List[PlayerActionStats] = [
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PlayerActionStats.from_actions(our_actions)
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]
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for cp in self.counterparties:
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action_tables.append(PlayerActionStats.from_actions(
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cp.legal_actions(node.state, params)
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))
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node.player_stats = action_tables
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def _rollout(self, state: MarketWorldState, params: FulfilmentPolicyParams, depth_remaining: int) -> float:
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total = 0.0
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cur = state
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for _ in range(max(depth_remaining, 0)):
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our_actions = build_our_actions(cur, params)
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our_action = self._rollout_our_action(cur, our_actions, params)
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cp_actions = tuple(cp.rollout_action(cur, self.rng) for cp in self.counterparties)
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nxt = self.cwm.transition(cur, (our_action, *cp_actions))
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total += self.cwm.reward(cur, our_action, nxt, params)
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cur = nxt
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if self.cwm.terminal(cur, 0):
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break
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total += self._leaf_value(cur, params)
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return total
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def _rollout_our_action(
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self,
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state: MarketWorldState,
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actions: Tuple[FulfilmentAction, ...],
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params: FulfilmentPolicyParams,
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) -> FulfilmentAction:
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exits = [a for a in actions if a.kind == ActionKind.FULL_EXIT]
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if exits:
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return exits[0]
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passive = [
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a for a in actions
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if a.kind in (ActionKind.PLACE, ActionKind.CANCEL_REPLACE) and a.post_only
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]
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if passive:
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return self.rng.choice(passive)
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return self.rng.choice(actions)
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def _leaf_value(self, state: MarketWorldState, params: FulfilmentPolicyParams) -> float:
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from malkhut.features import DefaultFeatureExtractor
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fv = DefaultFeatureExtractor().extract(state).values
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return (
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params.w_expected_pnl * fv.get("pnl_bps", 0.0)
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- params.w_adverse_selection * fv.get("orderflow_toxicity", 0.0)
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- params.w_tail_loss * abs(fv.get("mae_bps", 0.0))
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- params.w_time_decay * math.log1p(fv.get("seconds_held", 0.0))
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)
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def _root_policy(self, root: SMNode, params: FulfilmentPolicyParams, sims: int) -> PlannedPolicy:
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if not root.player_stats:
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fallback = FulfilmentAction(ActionKind.NOOP, None, None, 0, 0.0, 0)
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return PlannedPolicy(
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actions=(fallback,), probabilities=(1.0,),
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selected_action=fallback,
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diagnostics={"sims": sims, "reason": "unexpanded"},
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)
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our_stats = root.player_stats[0]
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visits = [max(0, n) for n in our_stats.visits]
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total = sum(visits)
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if total <= 0:
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probs = [1.0 / len(visits) for _ in visits]
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else:
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temp = max(params.root_temperature, 1e-6)
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raw = [(v / total) ** (1.0 / temp) for v in visits]
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s = sum(raw)
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probs = [x / max(s, 1e-12) for x in raw]
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entropy = -sum(p * math.log(max(p, 1e-12)) for p in probs)
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if entropy < params.min_root_entropy and len(probs) > 1:
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uniform = 1.0 / len(probs)
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mix = min(0.50, (params.min_root_entropy - entropy) / max(params.min_root_entropy, 1e-12))
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probs = [(1.0 - mix) * p + mix * uniform for p in probs]
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selected = self._sample_action(tuple(our_stats.actions), tuple(probs))
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return PlannedPolicy(
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actions=tuple(our_stats.actions),
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probabilities=tuple(probs),
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selected_action=selected,
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diagnostics={
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"sims": sims,
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"root_visits": root.visits,
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"entropy": entropy,
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"action_visits": visits,
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},
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)
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def _sample_action(
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self,
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actions: Tuple[FulfilmentAction, ...],
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probs: Tuple[float, ...],
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) -> FulfilmentAction:
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r = self.rng.random()
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cum = 0.0
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for a, p in zip(actions, probs):
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cum += p
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if r <= cum:
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return a
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return actions[-1]
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