Ensemble Elastic DQN: A Step Dependent Ensemble Approach for Reducing Overestimation in Deep Value-Based Reinforcement Learning
arXiv:2506.05716v2 Announce Type: replace-cross Abstract: Deep Q-Networks (DQN) can suffer from overestimation bias because bootstrapped targets use a maximisation operation over noisy value estimates. Ensemble-based methods and multi-step methods have each been used to improve the stability and sample efficiency of value-based reinforcement learning, but their interaction remains less well understood. This paper introduces Ensemble Elastic DQN (EEDQN), a value-based reinforcement learning algorithm that combines adaptive elastic multi-step returns with ensemble-based target aggregation. EEDQN
The continuous development in AI research demands ongoing improvements in reinforcement learning algorithms to enhance stability and sample efficiency, directly addressing current limitations of Deep Q-Networks.
Improving reinforcement learning algorithms like DQNs is critical for advancing the capabilities of autonomous AI agents, making them more robust and efficient for real-world applications.
This research introduces a refined approach that reduces overestimation bias in Deep Q-Networks, potentially leading to more reliable and effective AI models in practical deployments.
- · AI researchers
- · Reinforcement learning applications
- · AI agent developers
- · Less efficient RL algorithms
EEDQN improves the stability and sample efficiency of value-based reinforcement learning algorithms.
More reliable reinforcement learning could accelerate the development and deployment of autonomous AI agents across various industries.
Enhanced AI agent capabilities may lead to broader automation of complex tasks, impacting white-collar workflows and the SaaS layer.
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Read at arXiv cs.AI