An Examination of Multi-Pass Q-Networks for Deep Reinforcement Learning in Parameterised Action Spaces
The addressed paper presents a novel method, Multi-Pass Deep Q-Networks (MPDQN), to enhance reinforcement learning capabilities in environments with parameterised action spaces. These spaces incorporate discrete actions alongside continuous action-parameters, thus demanding algorithms that can effectively navigate and optimize within such complex domains. The authors critique and improve upon the Parameterised Deep Q-Networks (PDQN) method, which previously extended deep Q-networks for parameterised action spaces. PDQN suffers performance drawbacks due to its treatment of action-parameters as a single joint input, leading to computational inaccuracy and suboptimal decision-making.
A significant contribution of the paper lies in its analysis of PDQN, specifically highlighting how the shared joint input of action-parameters invalidates PDQN's theoretical underpinnings. This design flaw causes dependency of discrete action Q-values on all action-parameters, introducing what the authors term "false gradients." These false gradients adversely affect both the network training dynamics and the quality of the learned policy.
The authors propose the MPDQN as a remedy. The diverse architecture of MPDQN takes advantage of multiple passes through a single Q-network. By separating the input of action-parameters during each pass, MPDQN ensures that the dependence of Q-values is restricted only to relevant action-parameters. This mitigates the false gradient issue entirely without sacrificing the shared network weights that allow the transfer of learned features across different actions—a common practice to capture regularities within the environments efficiently.
The paper includes robust empirical evaluations of MPDQN against PDQN, separate Q-networks (SPDQN), QPAMDP, and PADDPG on benchmark environments such as the Platform domain, Robot Soccer Goal, and Half Field Offense. Across these domains, MPDQN consistently outperforms other models in terms of data efficiency and converged policy performance. Notably, the algorithm displays superior stability relative to PADDPG, which shows a tendency to converge prematurely to suboptimal solutions—a result corroborating the hypothesis that parallel optimization of discrete and continuous policy components can lead to performance degradation.
The academic and practical implications of this research are significant. Theoretically, the paper provides valuable insights into the structural challenges inherent in combining discrete and continuous action spaces within reinforcement learning. Practically, the improved performance of MPDQN over existing methods offers a potent tool for addressing a broader class of real-world problems where such combined action spaces exist, such as robotic soccer tasks and terrain-adaptive locomotion models.
Future explorations might investigate additional architectural enhancements or explore more diverse classes of environments to further generalize the findings. The possibility of leveraging similar multi-pass concepts in other reinforcement learning architectures could extend benefits beyond parameterised action domains, potentially improving optimization in tasks with hierarchical or multi-faceted decision requirements.
Overall, the methodological advancements outlined in this paper stand to bolster reinforcement learning's applicability, particularly in nuanced domains requiring finely-tuned action-space interactions.