Efficient Reinforcement Learning with Local Simulator Access: Unlocking Sample-Efficient Learning for Challenging MDPs

Core Concepts

Local simulator access enables sample-efficient reinforcement learning for MDPs with low coverability, including challenging settings like Exogenous Block MDPs, using only realizability of the optimal state-value function.

Abstract

The content discusses the power of local simulator access in reinforcement learning (RL) and presents new algorithms and guarantees for online RL with general function approximation.
Key highlights:

The authors introduce the SimGolf algorithm, which leverages local simulator access to achieve sample-efficient learning for MDPs with low coverability, requiring only realizability of the optimal state-action value function. This significantly relaxes the representation assumptions required by prior algorithms.
As a consequence, SimGolf is shown to make the notoriously challenging Exogenous Block MDP (ExBMDP) problem tractable in its most general form under local simulator access.
To address the computational inefficiency of SimGolf, the authors present a more practical algorithm called RVFS (Recursive Value Function Search), which achieves sample-efficient learning guarantees with general value function approximation under a strengthened statistical assumption called pushforward coverability.
RVFS explores by building core-sets with a novel value function-guided scheme, and can be viewed as a principled counterpart to successful empirical approaches like MCTS and AlphaZero that combine recursive search with value function approximation.

The key technical ideas include:

Using local simulator access to directly estimate Bellman backups, avoiding the double sampling problem.
Leveraging coverability and realizability conditions to obtain sample complexity guarantees.
Designing core-set construction schemes guided by value function approximation to enable computationally efficient exploration.
Overall, the work demonstrates how local simulator access can unlock new statistical and computational guarantees for reinforcement learning with general function approximation that were previously out of reach.

Stats

The following sentences contain key metrics or figures:
The total sample complexity in the RLLS framework is bounded by Õ(H^5 C_cov^2 log(|Q|/δ) / ε^4).
The total sample complexity in the RLLS framework is bounded by Õ(H^5 S^3 A^3 log|Φ| / ε^4).

Quotes

None.

Key Insights Distilled From

The Power of Resets in Online Reinforcement Learning

by Zakaria Mham... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15417.pdf

Deeper Inquiries

How can the polynomial dependence on problem parameters in the sample complexity bounds be further improved

To further improve the polynomial dependence on problem parameters in the sample complexity bounds, several strategies can be considered:

Refinement of Exploration Strategies: Developing more efficient exploration strategies can reduce the number of samples required to learn an optimal policy. Techniques such as optimism in the face of uncertainty, Thompson sampling, or adaptive exploration can help in this regard.

Improved Function Approximation: Utilizing more advanced function approximation methods, such as neural networks with better architectures or incorporating techniques like ensemble learning, can lead to more accurate value function estimates with fewer samples.

Enhanced Core-Set Construction: Refining the core-set construction process to select more informative state-action pairs can improve the efficiency of learning. This can involve incorporating more sophisticated algorithms for selecting core-set samples based on uncertainty estimates or value function gradients.

Optimization Algorithms: Using more efficient optimization algorithms for updating the value function estimates, such as stochastic gradient descent with adaptive learning rates or advanced optimization techniques like Adam, can help in faster convergence and reduced sample complexity.

By incorporating these strategies and potentially exploring new algorithmic innovations, it may be possible to further reduce the polynomial dependence on problem parameters in the sample complexity bounds.

What are the fundamental limits of online RL without local simulator access in terms of the representation conditions required for sample-efficient learning

The fundamental limits of online RL without local simulator access primarily revolve around the representation conditions required for sample-efficient learning. In the absence of local simulator access, online RL algorithms heavily rely on trajectory-based feedback, which limits their ability to efficiently explore and learn in complex environments. Some of the key limitations include:

High Sample Complexity: Without the additional information provided by local simulator access, online RL algorithms may require a large number of samples to learn an optimal policy, especially in high-dimensional state spaces or with complex dynamics.

Limited Exploration: Online RL algorithms without local simulator access may struggle with exploration in environments with sparse rewards or deceptive dynamics, leading to suboptimal policies or slow learning progress.

Sensitivity to Initial Conditions: The trajectory-based feedback in online RL can make the learning process sensitive to the initial conditions and the randomness in the environment, potentially leading to suboptimal solutions or slow convergence.

Difficulty in Generalization: Online RL algorithms may face challenges in generalizing learned policies to unseen states or tasks, especially when dealing with complex function approximation methods like neural networks.

Overall, the limitations of online RL without local simulator access highlight the importance of leveraging additional information sources and developing more robust algorithms to overcome these challenges and achieve sample-efficient learning.

How can the ideas of value function-guided core-set construction and recursive search be extended to other challenging RL settings beyond the ones considered in this work

The ideas of value function-guided core-set construction and recursive search can be extended to other challenging RL settings beyond the ones considered in this work by adapting and applying them in the following ways:

Transfer Learning: The core-set construction and recursive search techniques can be applied in transfer learning scenarios to leverage knowledge from related tasks or domains. By initializing core-sets with relevant samples and adapting the recursive search process, the algorithms can accelerate learning in new environments.

Multi-Agent Systems: Extending these ideas to multi-agent systems can involve developing collaborative core-set construction methods and recursive search strategies that account for the interactions between multiple agents. This can improve coordination and learning efficiency in complex multi-agent environments.

Hierarchical Reinforcement Learning: In hierarchical RL settings, the concepts of core-set construction and recursive search can be utilized at different levels of the hierarchy. By hierarchically organizing core-sets and recursive search processes, the algorithms can learn hierarchical policies efficiently.

Continuous Action Spaces: Adapting these techniques to continuous action spaces involves developing sampling and exploration strategies that are compatible with continuous actions. Techniques like Gaussian processes for uncertainty estimation and exploration can be integrated into the core-set construction and recursive search algorithms for continuous action spaces.

By creatively applying these ideas to diverse RL settings and domains, researchers can explore new avenues for improving learning efficiency and performance in a wide range of challenging environments.