Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead

2025-06-08

AI Summary

Policy gradient (PG) methods often converge to suboptimal local optima, especially in complex Markov decision processes (MDPs) with strong local traps. To address this, we propose an m-step lookahead tree search–enhanced PG framework that tightly integrates finite-depth Monte Carlo tree search (MCTS) with policy gradient updates, enabling trajectory-based local parameter updates exclusively at states visited under the current policy. Theoretically, we prove that increasing the lookahead depth *m* monotonically reduces the set of undesirable stationary points—including spurious local optima—and that this property holds under practical assumptions. We further establish convergence guarantees via worst-case performance analysis. Empirical evaluation on challenging MDPs—Ladder, Tightrope, and Gridworld—demonstrates that our method significantly improves policy performance, enables far-sighted decision-making, and effectively escapes local optima.

Technology Category

• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Classical policy gradient (PG) methods in reinforcement learning frequently converge to suboptimal local optima, a challenge exacerbated in large or complex environments. This work investigates Policy Gradient with Tree Search (PGTS), an approach that integrates an $m$-step lookahead mechanism to enhance policy optimization. We provide theoretical analysis demonstrating that increasing the tree search depth $m$-monotonically reduces the set of undesirable stationary points and, consequently, improves the worst-case performance of any resulting stationary policy. Critically, our analysis accommodates practical scenarios where policy updates are restricted to states visited by the current policy, rather than requiring updates across the entire state space. Empirical evaluations on diverse MDP structures, including Ladder, Tightrope, and Gridworld environments, illustrate PGTS's ability to exhibit"farsightedness,"navigate challenging reward landscapes, escape local traps where standard PG fails, and achieve superior solutions.

Problem

• Avoids local optima in policy gradient methods
• Integrates tree search for better policy optimization
• Improves worst-case performance in complex environments

Innovation

• Integrates m-step lookahead for optimization
• Reduces undesirable stationary points monotonically
• Updates policy based on visited states