Information-Directed Exploration for Deep Reinforcement Learning (1812.07544v2)

Abstract: Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.

• The paper introduces IDS as a novel exploration strategy integrated with deep Q-learning to tackle the exploration-exploitation dilemma in noisy environments.
• It presents a tractable approximation that addresses both homogeneous and heteroscedastic noise by combining parametric uncertainty with observation noise.
• Empirical results on Atari games demonstrate significant performance improvements over traditional methods, validating the effectiveness of the IDS approach.

Information-Directed Exploration for Deep Reinforcement Learning

The paper under review provides a comprehensive exploration of the application of Information-Directed Sampling (IDS) within Deep Reinforcement Learning (RL) frameworks. Its focus lies on addressing the exploration-exploitation dilemma prevalent in RL, particularly in contexts characterized by heteroscedastic noise. Classical exploration strategies, such as Upper Confidence Bound (UCB) algorithms and Thompson Sampling (TS), are typically effective in homogeneous settings, but they show limitations when dealing with heterogeneous noise — variability that depends on states and actions.

Key Contributions

1. IDS for RL: The authors propose the use of IDS, which has demonstrated effectiveness in the bandit setting, as a feasible exploration strategy in the RL domain. IDS provides a structured approach to balancing the immediate regret and the anticipated information gain, crucial for resolving the exploration-exploitation dilemma.
2. Tractable Approximation for Deep Q-Learning: The primary contribution of this work is the adaptation of IDS to Deep Q-Learning, including both homoscedastic and heteroscedastic variants. This novel implementation approximates the process in a manner that considers both parametric uncertainty and observation noise, generalized to deep learning scenarios.
3. Empirical Validation: The methodology was tested on Atari 2600 games, yielding significant improvements over existing state-of-the-art algorithms. By integrating an approximation of IDS with Bootstrapped DQN architecture and distributional RL mechanisms, the authors present empirical evidence suggesting that accounting for heteroscedastic noise can indeed enhance performance metrics.

Discussion on Results

The results indicate that the proposed IDS mechanism improves over traditional exploration strategies, such as TS, particularly in environments characterized by significant noise heteroscedasticity. For instance, when applied to Deep Q-Learning in Atari games, IDS not only surpasses the original DQN and Boostrapped DQN setups but also demonstrates competitive advantages compared to advanced algorithms like C51 or QR-DQN, boosting performance metrics by a notable margin. In terms of human-normalized performance scores, the mean score from employing IDS exceeds configurations that did not incorporate distributional estimations for the reward or that did not fully leverage heteroscedasticity in their algorithmic structure.

Implications and Future Work

The introduction of IDS in RL presents an intriguing avenue for dealing with the challenge of efficient exploration in uncertain environments. Practically, this paper sets a precedent for deploying informational metrics dynamically to direct learning processes in environments where noise is not uniformly distributed. Theoretically, this research opens up further exploration into the formulation of even more refined information-gain functions and their potential applications, such as continuous control or multi-agent environments.

Possible extensions of this work could focus on:

• Developing more scalable versions of IDS for RL that retain the computational efficiency even in high-dimensional state spaces.
• Exploring other environments beyond Atari to generalize the applicability of IDS.
• Investigating IDS under model-based RL frameworks as opposed to the primarily model-free approach demonstrated in this work.

In conclusion, this paper underscores the relevance of accounting for heteroscedastic noise and parametric uncertainty in designing exploratory strategies within deep RL frameworks, marking a significant step in advancing exploration methodologies in this field.