# Reinforcement Learning with Lookahead Information

arXiv: https://arxiv.org/abs/2406.02258 · HTML: https://mlsanity.com/paper/2406.02258/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 10 of 10)

- [Provably Efficient Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/1907.05388/) (2019)
- [Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited](https://mlsanity.com/paper/2010.03531/) (2020)
- [Policy Certificates: Towards Accountable Reinforcement Learning](https://mlsanity.com/paper/1811.03056/) (2018)
- [Settling the Sample Complexity of Online Reinforcement Learning](https://mlsanity.com/paper/2307.13586/) (2023)
- [The Power of Predictions in Online Control](https://mlsanity.com/paper/2006.07569/) (2020)
- [Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems](https://mlsanity.com/paper/2106.10497/) (2021)
- [Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity](https://mlsanity.com/paper/2210.12312/) (2022)
- [Semi-Bandit Learning for Monotone Stochastic Optimization](https://mlsanity.com/paper/2312.15427/) (2023)
- [The Value of Reward Lookahead in Reinforcement Learning](https://mlsanity.com/paper/2403.11637/) (2024)
- [Online Planning with Lookahead Policies](https://mlsanity.com/paper/1909.04236/) (2019)

## Built on by (most cited first, 8 of 8)

- [Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information](https://mlsanity.com/paper/2409.08434/) (2024)
- [Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security](https://mlsanity.com/paper/2504.07719/) (2025)
- [Minimax PAC Bounds for Learning in Exogenous Contextual MDPs](https://mlsanity.com/paper/2606.25170/) (2026)
- [Generalization and Optimization of SGD with Lookahead](https://mlsanity.com/paper/2509.15776/) (2025)
- [Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching](https://mlsanity.com/paper/2601.10418/) (2026)
- [Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding](https://mlsanity.com/paper/2602.00781/) (2026)
- [Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning](https://mlsanity.com/paper/2606.20107/) (2026)
- [Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead](https://mlsanity.com/paper/2609.11807/) (2026)

## Abstract (arXiv)

We study reinforcement learning (RL) problems in which agents observe the reward or transition realizations at their current state before deciding which action to take. Such observations are available in many applications, including transactions, navigation and more. When the environment is known, previous work shows that this lookahead information can drastically increase the collected reward. However, outside of specific applications, existing approaches for interacting with unknown environments are not well-adapted to these observations. In this work, we close this gap and design provably-efficient learning algorithms able to incorporate lookahead information. To achieve this, we perform planning using the empirical distribution of the reward and transition observations, in contrast to vanilla approaches that only rely on estimated expectations. We prove that our algorithms achieve tight regret versus a baseline that also has access to lookahead information - linearly increasing the amount of collected reward compared to agents that cannot handle lookahead information.