Benchmarking Batch Deep Reinforcement Learning Algorithms (1910.01708v1)

Abstract: Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment. Following this result, there have been several papers showing reasonable performances under a variety of environments and batch settings. In this paper, we benchmark the performance of recent off-policy and batch reinforcement learning algorithms under unified settings on the Atari domain, with data generated by a single partially-trained behavioral policy. We find that under these conditions, many of these algorithms underperform DQN trained online with the same amount of data, as well as the partially-trained behavioral policy. To introduce a strong baseline, we adapt the Batch-Constrained Q-learning algorithm to a discrete-action setting, and show it outperforms all existing algorithms at this task.

• The paper benchmarks various deep RL algorithms using a fixed dataset, highlighting significant extrapolation errors in off-policy methods.
• It adapts the BCQ algorithm for discrete-action environments, achieving superior results compared to online DQN and behavior policies.
• The study underscores the need for new methods to mitigate extrapolation errors, driving advancements in data-efficient RL techniques.

Benchmarking Batch Deep Reinforcement Learning Algorithms: A Technical Overview

The paper "Benchmarking Batch Deep Reinforcement Learning Algorithms" by Fujimoto et al. focuses on evaluating the effectiveness of batch deep reinforcement learning (RL) algorithms, particularly within environments where an agent is restricted to learn from a fixed dataset generated by a single, partially-trained behavior policy. This research is particularly relevant in real-world applications where acquiring new data is challenging or costly, necessitating RL systems to operate with pre-existing data samples.

Key Contributions and Findings

The paper comprehensively benchmarks several prominent RL algorithms under a unified setting utilizing the Atari domain. One of the main findings is the underperformance of many existing off-policy algorithms when applied in a batch setting compared to two baselines: a Deep Q-Network (DQN) trained online and the behavior policy itself. This suggests standard off-policy algorithms struggle to generalize from fixed datasets, owing to extrapolation error—this arises when the algorithms predict outcomes for state-action pairs that are not sufficiently represented within the dataset.

To address these shortcomings, the authors adapted the Batch-Constrained Q-learning algorithm to handle discrete-action environments more effectively. The discrete-action version of BCQ introduced in this paper demonstrated superior performance across tested games, largely exceeding other methods' results including variations like KL-Control.

Strong Numerical Results

The robustness of the discrete-action BCQ algorithm is highlighted by its consistent outperformance across all environments tested. For instance, the algorithm achieved results that surpass the noisy behavior policy and matched or exceeded the performance of the online DQN agent. This is particularly significant as BCQ operates without additional interaction with the environment, purely leveraging the data set generated by the behavior policy.

Theoretical Implications

The findings underscore the inefficiencies in current batch RL models when faced with datasets that stem from limited exploration policies. The demonstrated prevalence of extrapolation errors suggests a need for developing methods that can effectively learn within constrained settings by mitigating these prediction errors. The paper positions BCQ as a potential baseline for further algorithmic advances that could address the extrapolation challenge, pointing towards avenues where robust imitation learning could integrate more effectively with reinforcement strategies.

Future Directions

Looking forward, there is potential for improvement in batch reinforcement learning algorithms to better address datasets with limited diversity and exploration coverage. Research could focus on enhancing generative modeling techniques or integrating ensemble-based approaches that capture variances more effectively, as highlighted by QR-DQN’s performance improvement in this domain through distributional RL techniques. The implications of this research encourage exploration in data-efficient methods and exploration strategies that optimize batch learning without relying solely on data diversity.

In conclusion, this paper provides critical insights into the limitations of current batch RL methodologies, advocating for innovative approaches that exploit dataset constraints while maintaining performance levels comparable to online learning paradigms. Building on these foundational findings could substantialize advancements in AI systems that rely on batch learning for decision-making tasks across constrained, data-limited environments.