# A PAC RL Algorithm for Episodic POMDPs

arXiv: https://arxiv.org/abs/1605.08062 · HTML: https://mlsanity.com/paper/1605.08062/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 30 of 37)

- [When Is Partially Observable Reinforcement Learning Not Scary?](https://mlsanity.com/paper/2204.08967/) (2022)
- [RL for Latent MDPs: Regret Guarantees and a Lower Bound](https://mlsanity.com/paper/2102.04939/) (2021)
- [PAC Reinforcement Learning for Predictive State Representations](https://mlsanity.com/paper/2207.05738/) (2022)
- [Provable Reinforcement Learning with a Short-Term Memory](https://mlsanity.com/paper/2202.03983/) (2022)
- [Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems](https://mlsanity.com/paper/2206.12020/) (2022)
- [Learning in Observable POMDPs, without Computationally Intractable Oracles](https://mlsanity.com/paper/2206.03446/) (2022)
- [Sample-Efficient Reinforcement Learning of Partially Observable Markov Games](https://mlsanity.com/paper/2206.01315/) (2022)
- [Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2205.13589/) (2022)
- [Planning in Observable POMDPs in Quasipolynomial Time](https://mlsanity.com/paper/2201.04735/) (2022)
- [Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms](https://mlsanity.com/paper/2209.14990/) (2022)
- [Future-Dependent Value-Based Off-Policy Evaluation in POMDPs](https://mlsanity.com/paper/2207.13081/) (2022)
- [An Instrumental Variable Approach to Confounded Off-Policy Evaluation](https://mlsanity.com/paper/2212.14468/) (2022)
- [Sublinear Regret for Learning POMDPs](https://mlsanity.com/paper/2107.03635/) (2021)
- [Learning in POMDPs is Sample-Efficient with Hindsight Observability](https://mlsanity.com/paper/2301.13857/) (2023)
- [Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency](https://mlsanity.com/paper/2204.09787/) (2022)
- [Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency](https://mlsanity.com/paper/2205.13476/) (2022)
- [Reinforcement Learning in Reward-Mixing MDPs](https://mlsanity.com/paper/2110.03743/) (2021)
- [Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings](https://mlsanity.com/paper/2206.12081/) (2022)
- [Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring](https://mlsanity.com/paper/2303.08271/) (2023)
- [Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning](https://mlsanity.com/paper/2311.12244/) (2023)
- [On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation](https://mlsanity.com/paper/2402.14703/) (2024)
- [Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP](https://mlsanity.com/paper/2306.12356/) (2023)
- [Statistical Tractability of Off-policy Evaluation of History-dependent Policies in POMDPs](https://mlsanity.com/paper/2503.01134/) (2025)
- [Provably Efficient UCB-type Algorithms For Learning Predictive State Representations](https://mlsanity.com/paper/2307.00405/) (2023)
- [RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation](https://mlsanity.com/paper/2406.01389/) (2024)
- [Reward-Mixing MDPs with a Few Latent Contexts are Learnable](https://mlsanity.com/paper/2210.02594/) (2022)
- [Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight](https://mlsanity.com/paper/2307.02884/) (2023)
- [Tractable Offline Learning of Regular Decision Processes](https://mlsanity.com/paper/2409.02747/) (2024)
- [Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition](https://mlsanity.com/paper/2305.00654/) (2023)
- [Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting](https://mlsanity.com/paper/2410.01331/) (2024)

## Abstract (arXiv)

Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially observable RL algorithm with a polynomial bound on the number of episodes on which the algorithm may not achieve near-optimal performance. Our algorithm is suitable for an important class of episodic POMDPs. Our approach builds on recent advances in method of moments for latent variable model estimation.