The Power of Resets in Online Reinforcement Learning (2404.15417v2)

Abstract: Simulators are a pervasive tool in reinforcement learning, but most existing algorithms cannot efficiently exploit simulator access -- particularly in high-dimensional domains that require general function approximation. We explore the power of simulators through online reinforcement learning with {local simulator access} (or, local planning), an RL protocol where the agent is allowed to reset to previously observed states and follow their dynamics during training. We use local simulator access to unlock new statistical guarantees that were previously out of reach: - We show that MDPs with low coverability (Xie et al. 2023) -- a general structural condition that subsumes Block MDPs and Low-Rank MDPs -- can be learned in a sample-efficient fashion with only $Q^{\star}$-realizability (realizability of the optimal state-value function); existing online RL algorithms require significantly stronger representation conditions. - As a consequence, we show that the notorious Exogenous Block MDP problem (Efroni et al. 2022) is tractable under local simulator access. The results above are achieved through a computationally inefficient algorithm. We complement them with a more computationally efficient algorithm, RVFS (Recursive Value Function Search), which achieves provable sample complexity guarantees under a strengthened statistical assumption known as pushforward coverability. RVFS can be viewed as a principled, provable counterpart to a successful empirical paradigm that combines recursive search (e.g., MCTS) with value function approximation.

• The paper presents two innovative algorithms that incorporate local simulator resets to boost learning efficiency in online reinforcement learning.
• SimGolf uses global optimism and local simulator access to tackle challenges in low coverability and ExBMDP scenarios.
• RVFS applies a recursive value search strategy with core-set construction to significantly improve exploration in complex state spaces.

Exploring Reinforcement Learning: A Dive into Local Planning with Simulators

Introduction to Local Simulator Access in RL

Reinforcement learning (RL) algorithms typically operate online without making full use of additional information that could be accessed through simulators. This paper addresses the underexplored area of online RL with local simulator access (RLLS) where an agent can reset to previously visited states, a nuance that diverts from typical online RL where only trajectory-based feedback is available. Recognizing the potential of using simulators more effectively can become instrumental in enhancing learning efficiency and developing new computational algorithms.

Enhancing Learning with Local Simulators: Theoretical Insights

By incorporating local planning into the learning process, the paper presents two distinct algorithms, highlighting their impact on learning in environments described by Markov Decision Processes (MDPs):

• SimGolf Algorithm: This proves the feasibility of using local simulator access to realize sample-efficient learning under certain conditions, such as low coverability and $Q^{\star}$-realizability. The algorithm's core approach is to leverage global optimism combined with access to the local simulator to effectively navigate and learn from the state space. Notably, it addresses the Exogenous Block MDP (ExBMDP) problem, demonstrating that it can be managed more tractably with this local planning approach.
• Recursive Value Function Search (RVFS): Building on the inefficiencies of SimGolf in computation, RVFS introduces a more efficient approach under the strengthened notion of pushforward coverability. It employs a recursive exploration mechanism using value function approximation, which is pivotal for environments that require consideration of complex state interactions and dynamics. This algorithm exemplifies a significant advancement in computationally efficient learning algorithms that utilize local simulators.

Practical Implications and Theoretical Contributions

The introduction of RVFS and its implications for practical RL applications mark a significant theoretical advance. It aligns with successful empirical frameworks like Monte Carlo Tree Search (MCTS) and AlphaZero, which similarly utilize recursive planning to improve performance in complex game environments. The RVFS algorithm, with its emphasis on recursive search and core-set construction, offers a provably efficient solution that could bridge the gap between theoretical robustness and practical efficacy.

Moreover, these approaches address the suboptimal state exploration challenge, providing structured methodologies to explore state spaces effectively, thereby optimizing the learning routines. The computational insights from RVFS, especially, set a foundation for future explorations into more scalable and efficient RL algorithms that exploit local simulators in high-dimensional settings.

Future Directions

The deployment of local simulator access in RL opens numerous pathways for future research, particularly in extending these methodologies to more generalized settings and other forms of dynamic environments. Enhancing the sample and computational efficiency of these algorithms remains an area ripe for development, with the potential to further refine the balance between exploration efficiency and computational resources.

Additionally, applying these insights to other complex MDPs, beyond the ExBMDP problems tackled in this paper, could widen the applicability of these algorithms, making them a standard toolset in advanced RL applications. Further investigations into the theoretical limits of these methods will be crucial to understanding their full potential and limitations.

In summary, this paper not only introduces innovative approaches to leveraging simulators in RL but also opens up a discourse on future possibilities in simulator-based algorithm designs, marking a significant step forward in the practical deployment of advanced reinforcement learning techniques.