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Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning

arXiv:2602.12375v1 Announce Type: cross Abstract: Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses, propagating local uncertainties around rewards. However, this approach only increases the value bonus for an action retroactively, after seeing a higher reward bonus from that state and action. Such an approach does not encourage the agent to visit a state and action for the first time. In this work, we introduce an algorithm for exploration called Value Bonuses with Ensemble errors (VBE), that maintains an ensemble of random action-value functions (RQFs). VBE uses the errors in the estimation of these RQFs to design value bonuses that provide first-visit optimism and deep exploration. The key idea is to design the rewards for these RQFs in such a w

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Abdul Wahab, Raksha Kumaraswamy, Martha White · · 1 min read · 9 views
#cs.LG #cs.AI

arXiv:2602.12375v1 Announce Type: cross Abstract: Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses, propagating local uncertainties around rewards. However, this approach only increases the value bonus for an action retroactively, after seeing a higher reward bonus from that state and action. Such an approach does not encourage the agent to visit a state and action for the first time. In this work, we introduce an algorithm for exploration called Value Bonuses with Ensemble errors (VBE), that maintains an ensemble of random action-value functions (RQFs). VBE uses the errors in the estimation of these RQFs to design value bonuses that provide first-visit optimism and deep exploration. The key idea is to design the rewards for these RQFs in such a way that the value bonus can decrease to zero. We show that VBE outperforms Bootstrap DQN and two reward bonus approaches (RND and ACB) on several classic environments used to test exploration and provide demonstrative experiments that it can scale easily to more complex environments like Atari.

Executive Summary

The article 'Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning' introduces a novel algorithm called Value Bonuses with Ensemble errors (VBE) designed to enhance exploration in reinforcement learning (RL). The VBE algorithm addresses the limitation of traditional optimistic value estimates by using an ensemble of random action-value functions (RQFs) to create value bonuses that encourage first-visit optimism and deep exploration. The authors demonstrate that VBE outperforms existing methods such as Bootstrap DQN, RND, and ACB in classic RL environments and scales effectively to complex environments like Atari. The key innovation lies in the design of rewards for RQFs, allowing the value bonus to decrease to zero, thereby promoting more efficient exploration.

Key Points

  • ▸ Introduction of the VBE algorithm for improved exploration in RL.
  • ▸ Use of an ensemble of RQFs to generate value bonuses.
  • ▸ Design of rewards to allow value bonuses to decrease to zero.
  • ▸ Outperformance of existing methods in classic and complex environments.

Merits

Innovative Approach

The VBE algorithm introduces a novel method for exploration in RL by leveraging ensemble errors, which addresses the limitations of traditional optimistic value estimates.

Empirical Validation

The article provides empirical evidence demonstrating the superiority of VBE over existing methods in both classic and complex environments, enhancing its credibility.

Scalability

The VBE algorithm is shown to scale effectively to more complex environments like Atari, indicating its potential for real-world applications.

Demerits

Complexity

The use of an ensemble of RQFs adds computational complexity to the algorithm, which may limit its practical applicability in resource-constrained settings.

Generalization

While the article demonstrates the effectiveness of VBE in specific environments, further research is needed to validate its performance across a broader range of RL tasks.

Theoretical Foundations

The article could benefit from a more detailed theoretical analysis of the VBE algorithm to provide a deeper understanding of its underlying principles.

Expert Commentary

The article presents a significant advancement in the field of reinforcement learning by introducing the VBE algorithm, which effectively addresses the challenge of exploration. The use of an ensemble of random action-value functions to generate value bonuses is a novel and innovative approach that sets it apart from traditional methods. The empirical results demonstrating the superiority of VBE over existing algorithms in both classic and complex environments are particularly compelling. However, the increased computational complexity introduced by the ensemble method is a notable limitation that may impact its practical applicability. Additionally, while the article provides a solid foundation for the VBE algorithm, further theoretical analysis and validation across a broader range of RL tasks would strengthen its credibility. Overall, the article makes a valuable contribution to the field and opens up new avenues for research in exploration strategies for reinforcement learning.

Recommendations

  • ✓ Further theoretical analysis of the VBE algorithm to provide a deeper understanding of its underlying principles.
  • ✓ Validation of the VBE algorithm across a broader range of RL tasks to assess its generalizability and robustness.
  • ✓ Exploration of methods to reduce the computational complexity of the VBE algorithm to enhance its practical applicability.

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