mlsanityData to 14 Oct 2026

Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead

arXiv 2609.11807 ↗First posted 10 Sep 2026stat.ML cs.LGcited by 0

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

Abstract (arXiv)

We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of actions before deciding its course of action. Although look-ahead can substantially improve achievable performance, [1] showed that optimal planning with multi-step transition look-ahead is NP-hard. However, this hardness was established using a discount factor close to one. It was therefore unknown whether the problem remains hard for every discount factor, and whether near-optimal planning can nevertheless be performed efficiently. We resolve both questions. First, we show that for every fixed discount factor, exact planning remains NP-hard. Second, we introduce a randomized polynomial-time approximation scheme for every fixed look-ahead depth. Third, we extend our approach to account for unknown transitions. We empirically validate the soundness of our results on the wind-farm storage-control benchmark of [2], showing that our approach, optimally accounting for -step look-ahead information, offers substantially better performance than existing algorithms. [1] Corentin Pla, Hugo Richard, Marc Abeille, Nadav Merlis, Vianney Perchet : On the Hardness of Reinforcement Learning with Transition Look-Ahead [2] Chenbei Lu, Zaiwei Chen, Tongxin Li, Chenye Wu, Adam Wierman : Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 32 references that are in the map. First posted on arXiv 10 Sep 2026.

Builds on

19 of 32 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Learning in Markov Decision Processes with Exogenous Dynamics3 Mar
  2. 2025On the Hardness of Reinforcement Learning with Transition Look-Ahead22 Oct
  3. 2025Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach21 Oct

Other references · 16

  1. 2017Minimax Regret Bounds for Reinforcement Learningcited by 503
  2. 2018Is Q-learning Provably Efficient?cited by 487
  3. 2020Algorithms with Predictionscited by 153
  4. 2020Nearly Minimax Optimal Reinforcement Learning for Discounted MDPscited by 42
  5. 2013On the Complexity of Solving Markov Decision Problemscited by 31
  6. 2020Regret Bounds for Discounted MDPscited by 19
  7. 2018On the Complexity of Value Iterationcited by 8
  8. 2023Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictionscited by 8
  9. 2024Reinforcement Learning with Lookahead Informationcited by 8
  10. 2025On Tradeoffs in Learning-Augmented Algorithmscited by 7
  11. 2022On Preemption and Learning in Stochastic Schedulingcited by 6
  12. 2024The Value of Reward Lookahead in Reinforcement Learningcited by 6
4 more
  1. 2020Hitting the High Notes: Subset Selection for Maximizing Expected Order Statisticscited by 4
  2. 2025Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Searchcited by 4
  3. 2017Lower Bound On the Computational Complexity of Discounted Markov Decision Problemscited by 3
  4. 2026Minimax PAC Bounds for Learning in Exogenous Contextual MDPscited by 1

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  1. 2026Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching
  2. 2026Learning from the Near Future: Temporal Self-Distillation for RLVR
  3. 2025Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead
  4. 2024Highway Reinforcement Learning
  5. 2026Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Starts
  6. 2025Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning
  7. 2023Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
  8. 2025Reinforcement Learning with Random Time Horizons