Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach (2202.00063v3)

Abstract: We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with block-structured dynamics (i.e., Block MDPs), where rich observations are generated from a set of unknown latent states. BRIEE interleaves latent states discovery, exploration, and exploitation together, and can provably learn a near-optimal policy with sample complexity scaling polynomially in the number of latent states, actions, and the time horizon, with no dependence on the size of the potentially infinite observation space. Empirically, we show that BRIEE is more sample efficient than the state-of-art Block MDP algorithm HOMER and other empirical RL baselines on challenging rich-observation combination lock problems that require deep exploration.

• The paper introduces Briee, a novel model-free algorithm for efficient representation learning and policy optimization in Block MDPs with hidden latent states.
• Briee achieves sample complexity that depends polynomially on the number of latent states and actions, notably independent of the observation space size.
• Empirical evaluations show Briee outperforms state-of-the-art methods like Homer, demonstrating superior sample efficiency in complex environments like the combination lock problem.

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach

The paper "Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach" introduces Briee, a novel algorithm tailored for reinforcement learning within Block Markov Decision Processes (Block MDPs). In these environments, observations arise from a set of latent states that are not directly observable. This work presents a method to efficiently learn near-optimal policies in such settings by leveraging representation learning without a predefined model of the observation space.

Overview and Key Contributions

Briee embodies a model-free approach to representation learning in reinforcement learning and incorporates mechanisms for exploration and exploitation. It is notably capable of learning a near-optimal policy with sample complexity that grows polynomially with respect to the number of latent states, actions, and the time horizon. Importantly, it eschews reliance on the often quite large observation space, which may be infinite.

Key contributions of the paper include:

1. Algorithm Design: The design of Briee offers a sample-efficient solution for Block MDPs without explicitly modeling the observation space. This is a significant departure from prior approaches that rely heavily on model-based techniques, thus making Briee suitable for high-dimensional, complex environments.
2. No Dependence on Observation Space Size: The algorithm’s sample complexity is independent of the observation space size, focusing instead on meaningful representations learned directly from interactions with the environment.
3. No Assumptions on Reachability: Unlike previous frameworks, Briee does not assume that each latent state is reachable with a certain probability, thereby broadening its applicability and robustness.

Empirical Evaluation

The empirical evaluation positions Briee against state-of-the-art algorithms such as Homer and other baseline reinforcement learning techniques in challenging environments that demand deep exploration. Specifically, Briee demonstrates superior sample efficiency, solving the challenging combination lock problem up to greater time horizons and consistently outperforming Homer.

Implications and Future Work

The paper’s findings suggest that representation learning can effectively bridge the gap between theoretical efficiency and practical applicability in reinforcement learning. This balance, especially in environments characterized by complex observation spaces, signifies substantial progress in efficiently learning policies without relying excessively on model-based strategies.

The authors hint at potential extensions of the theoretical applicability of Briee beyond Block MDPs to more general settings including low-rank MDPs, exemplified by encouraging empirical outcomes. Future investigations could refine the theoretical foundations underpinning Briee’s efficacy across more diverse environments.

Conclusion

The introduction of Briee represents an impactful advance in reinforcement learning methods for Block MDPs, particularly in its model-free approach to representation learning. Moving forward, this method can inspire subsequent work that further diminishes reliance on model-based assumptions in environments with rich observational data. The tractability and efficiency demonstrated in empirical evaluations suggest promising directions for research seeking to harness representation learning across broader categories of MDPs.