# Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition

arXiv: https://arxiv.org/abs/2004.10019 · HTML: https://mlsanity.com/paper/2004.10019/

## Key facts

- Cited by 127 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 30 of 127)

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- [Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes](https://mlsanity.com/paper/2212.06132/) (2022)
- [Near-Optimal Offline Reinforcement Learning via Double Variance Reduction](https://mlsanity.com/paper/2102.01748/) (2021)
- [Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game](https://mlsanity.com/paper/2205.15512/) (2022)
- [Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning](https://mlsanity.com/paper/2110.04645/) (2021)
- [The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2305.16589/) (2023)
- [Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap](https://mlsanity.com/paper/2102.04692/) (2021)
- [The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces](https://mlsanity.com/paper/2106.03352/) (2021)
- [UCB Momentum Q-learning: Correcting the bias without forgetting](https://mlsanity.com/paper/2103.01312/) (2021)
- [Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP](https://mlsanity.com/paper/2101.12745/) (2021)
- [Settling the Sample Complexity of Online Reinforcement Learning](https://mlsanity.com/paper/2307.13586/) (2023)
- [The Efficacy of Pessimism in Asynchronous Q-Learning](https://mlsanity.com/paper/2203.07368/) (2022)
- [A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost](https://mlsanity.com/paper/2101.00494/) (2021)
- [An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap](https://mlsanity.com/paper/2103.12690/) (2021)
- [MADE: Exploration via Maximizing Deviation from Explored Regions](https://mlsanity.com/paper/2106.10268/) (2021)
- [First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach](https://mlsanity.com/paper/2112.03432/) (2021)
- [On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.05707/) (2021)
- [The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond](https://mlsanity.com/paper/2305.10697/) (2023)
- [Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret](https://mlsanity.com/paper/2104.11186/) (2021)
- [Fast Rates for Maximum Entropy Exploration](https://mlsanity.com/paper/2303.08059/) (2023)
- [Sharp Analysis for KL-Regularized Contextual Bandits and RLHF](https://mlsanity.com/paper/2411.04625/) (2024)
- [Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost](https://mlsanity.com/paper/2202.06385/) (2022)
- [A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/2209.15634/) (2022)
- [Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes](https://mlsanity.com/paper/2306.16394/) (2023)
- [Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting](https://mlsanity.com/paper/2105.08024/) (2021)

## Abstract (arXiv)

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with S states, A actions, and episode length H. We propose a model-free algorithm UCB-Advantage and prove that it achieves tildeO(√H2SAT) regret where T = KH and K is the number of episodes to play. Our regret bound improves upon the results of [Jin et al., 2018] and matches the best known model-based algorithms as well as the information theoretic lower bound up to logarithmic factors. We also show that UCB-Advantage achieves low local switching cost and applies to concurrent reinforcement learning, improving upon the recent results of [Bai et al., 2019].