Randomized Prior Functions for Deep Reinforcement Learning (1806.03335v2)

Abstract: Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member. We prove that this approach is efficient with linear representations, provide simple illustrations of its efficacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts.

• The paper introduces a novel method using randomized prior functions to embed prior knowledge in ensemble deep RL models.
• The paper demonstrates that integrating these priors into deep Q-networks significantly improves uncertainty estimation and exploration in complex settings.
• The paper provides strong theoretical and empirical evidence showing enhanced performance over traditional bootstrap techniques in challenging environments.

An Expert Overview of "Randomized Prior Functions for Deep Reinforcement Learning"

This paper presents a novel approach to addressing uncertainty in deep reinforcement learning (RL) by introducing randomized prior functions. The authors propose a method for incorporating prior knowledge into ensemble models within a reinforcement learning context, enhancing the agent's capability for exploration and improving the efficacy of decision-making processes involving uncertainty.

Core Concepts and Methodology

The core innovation detailed in this paper is the integration of a "prior" in ensemble models, specifically through the use of randomized prior functions added to each ensemble member. Traditional methods such as bootstrap sampling provide uncertainty estimation based solely on observed data, which lacks the flexibility needed for effective exploration in sequential decision problems. The paper highlights these limitations and demonstrates that the inclusion of a randomized, untrainable prior function significantly improves performance, especially in challenging environments.

The paper provides a theoretical underpinning by proving the efficiency of this approach with linear representations and supports these claims with empirical results from nonlinear settings. Their experiments show that the proposed method scales more effectively to large-scale problems than existing techniques.

Key Contributions

1. Randomized Prior Functions: The addition of a prior to each ensemble member allows the model to maintain epistemic uncertainty in unexplored state-action spaces, enabling better exploration strategies.
2. Practical Implementation: The paper demonstrates the feasibility of implementing these prior functions within the standard framework of deep Q-networks (DQN), leveraging the strengths of bootstrapped DQN while mitigating its weaknesses in expressing initial uncertainty.
3. Robust Theoretical Foundation: They provide a rigorous mathematical foundation for the proposed method, illustrating that this approach yields results consistent with what one would expect from Gaussian linear models but extended into the complex spaces of deep learning models.
4. Extensive Experimental Validation: Through a series of experiments across different domains, including sparse reward tasks like Montezuma's Revenge, the method shows substantial improvements over baseline approaches.

Implications

The implications of this research are significant, providing a more robust strategy for RL in environments where uncertainty plays a critical role. By addressing the need for priors beyond simplistic model-free methods, agents are better equipped to handle exploration tasks, thereby expanding the potential applications of RL in real-world scenarios.

Notably, the introduction of randomized prior functions offers a pathway to integrate domain knowledge into RL systems seamlessly, bridging the gap between model-free and model-based approaches in RL. This could lead to more efficient learning while reducing the computational burden often associated with scalable RL systems.

Future Directions

The paper closes by pointing toward several avenues for future research, such as optimizing or "meta-learning" the prior functions utilized within these networks, exploring more sophisticated priors, and distilling ensemble processes into more streamlined, single-network architectures.

In conclusion, this paper outlines both theoretical and practical advances in the integration of prior knowledge into deep RL models, marking a step forward in developing agents capable of handling complex, uncertain environments with greater efficacy. This research enhances understanding of how prior knowledge can be systematically exploited in reinforcement learning to achieve superior exploration and learning capabilities.