mcts_node.py

import math
from thoughtsculpt.thought_evaluator import ThoughtEvaluator
from thoughtsculpt.thought_generator import ThoughtGenerator

class MCTSNode:
    def __init__(self, solution, parent=None):
        self.solution = solution
        self.parent = parent
        self.children = []
        self.num_visits = 0
        self.total_reward = 0.0

    def is_terminal(self):
        # Implement logic to determine if the node is a terminal node
        pass

    def is_fully_expanded(self):
        # Implement logic to determine if the node is fully expanded
        pass

    def expand(self, api_key, task_description):
        thought_generator = ThoughtGenerator(api_key)
        feedback_text, _ = ThoughtEvaluator(api_key).evaluate(self.solution, task_description)
        candidate_solution = thought_generator.generate(self.solution, feedback_text, task_description)[0]
        child = MCTSNode(candidate_solution, self)
        self.children.append(child)
        return child

    def select_child(self):
        ucb1_scores = [child.ucb1_score(self.num_visits) for child in self.children]
        return self.children[ucb1_scores.index(max(ucb1_scores))]

    def ucb1_score(self, parent_visits):
        exploration_factor = math.sqrt(2 * math.log(parent_visits) / self.num_visits)
        return (self.total_reward / self.num_visits) + exploration_factor

    def backpropagate(self, reward):
        self.num_visits += 1
        self.total_reward += reward
        if self.parent:
            self.parent.backpropagate(reward)

    def best_child(self):
        return max(self.children, key=lambda child: child.total_reward / child.num_visits)

    def evaluate(self, api_key, task_description):
        thought_evaluator = ThoughtEvaluator(api_key)
        _, feedback_score = thought_evaluator.evaluate(self.solution, task_description)
        return feedback_score