An Optimistic Perspective on Offline Reinforcement Learning (1907.04543v4)

Abstract: Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-policy deep RL algorithms, even when trained solely on this fixed dataset, outperform the fully trained DQN agent. To enhance generalization in the offline setting, we present Random Ensemble Mixture (REM), a robust Q-learning algorithm that enforces optimal BeLLMan consistency on random convex combinations of multiple Q-value estimates. Offline REM trained on the DQN replay dataset surpasses strong RL baselines. Ablation studies highlight the role of offline dataset size and diversity as well as the algorithm choice in our positive results. Overall, the results here present an optimistic view that robust RL algorithms trained on sufficiently large and diverse offline datasets can lead to high quality policies. The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.

An Optimistic Perspective on Offline Reinforcement Learning

The paper "An Optimistic Perspective on Offline Reinforcement Learning" by Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi investigates the efficacy of offline reinforcement learning (RL) using data logged from Deep Q-Network (DQN) agents. The researchers demonstrate that recent off-policy deep reinforcement learning algorithms can effectively utilize offline datasets to outperform fully-trained DQN agents, particularly emphasizing the role of large and diverse datasets in achieving high-quality policy learning.

The paper leverages the DQN Replay Dataset, consisting of 60 Atari 2600 games' logged interactions, and introduces an algorithm called Random Ensemble Mixture (REM). REM is a robust $Q$-learning algorithm that enhances generalization by enforcing optimal BeLLMan consistency across random combinations of $Q$-value estimates. Their empirical evaluations show that REM surpasses several baseline RL approaches on the Atari benchmark, suggesting that effective exploitation of large datasets can yield superior performance.

The paper provides rigorous numerical results underscoring the potential of offline RL. For example, offline QR-DQN, one of the evaluated algorithms, trained on the replay dataset, outperforms the best policies identified during the original data collection phase. Additionally, the results highlight that offline REM not only surpasses existing offline and online baselines but also provides a more computationally efficient avenue for RL research, eliminating the need for costly environment interactions typically required in online RL settings.

The implications of this research are both practical and theoretical. Practically, it suggests that offline RL can mitigate the inherent challenges of data collection in real-world applications, such as robotics, healthcare, and autonomous systems. Theoretically, it invites further exploration into algorithms that can generalize effectively from fixed datasets, suggesting possible directions like combining REM with other RL approaches such as distributional RL and behavior regularization techniques.

Looking ahead, the research sets a stage for future developments in AI by underlining the importance of dataset quality and size in offline RL and demonstrating the potential of ensemble methods for value-based estimation. It also opens up new possibilities for efficient RL training regimes that pretrain agents on static datasets before deployment in dynamic environments, thereby enhancing both sample efficiency and application feasibility.

Overall, this paper provides valuable insights into designing robust RL algorithms capable of leveraging large offline datasets, presenting an optimistic perspective on the potential advancements and applications of offline reinforcement learning.