mlsanityData to 14 Oct 2026

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead

arXiv 2506.07054 ↗First posted 8 Jun 2025cs.LG cs.AIcited by 1

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 18 references that are in the map. First posted on arXiv 8 Jun 2025.

Builds on

9 of 18 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Elementary Analysis of Policy Gradient Methods4 Apr
  2. 2024On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes11 Mar

Other references · 7

  1. 2015Trust Region Policy Optimizationcited by 2,580
  2. 2020On the Global Convergence Rates of Softmax Policy Gradient Methodscited by 264
  3. 2018Provably Efficient Maximum Entropy Explorationcited by 242
  4. 2022On the Convergence Rates of Policy Gradient Methodscited by 119
  5. 2022Finite-time analysis of single-timescale actor-criticcited by 32
  6. 2019The Value Function Polytope in Reinforcement Learningcited by 29
  7. 2018Beyond the One Step Greedy Approach in Reinforcement Learningcited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentumcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026OPTS-TTPO: Enhancing Finite-Sample Policy-Gradient Learning with Tree Search
  2. 2025SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
  3. 2024Behind the Myth of Exploration in Policy Gradients
  4. 2024The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
  5. 2025Efficient On-Policy Reinforcement Learning via Exploration of Sparse Parameter Space
  6. 2026Policy Gradient Methods for Non-Markovian Reinforcement Learning
  7. 2026EPIG-Tree: Compute-Optimal Branching for Gradient-Efficient Reinforcement Learning
  8. 2025Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search