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FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control

arXiv:2603.12612v1 Announce Type: new Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a formidable challenge, as the ``curse of dimensionality'' induces severe exploration inefficiency and training instability in expansive action spaces. Consequently, recent high-throughput paradigms have largely converged on deterministic policy gradients combined with massive parallel simulation. We challenge this compromise with FastDSAC, a framework that effectively unlocks the potential of maximum entropy stochastic policies for complex continuous control. We introduce Dimension-wise Entropy Modulation (DEM) to dynamically redistribute the exploration budget and enforce diversity, alongside a continuous distributional critic tailored to ensure value fidelity and mitigate high-dimensional value overestimation. Extensive evaluations on HumanoidBench and other continuous control tasks demonstrate that rigorously designed stochastic policies

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Jun Xue, Junze Wang, Xinming Zhang, Shanze Wang, Yanjun Chen, Wei Zhang · · 1 min read · 8 views
#cs.LG #cs.AI

arXiv:2603.12612v1 Announce Type: new Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a formidable challenge, as the ``curse of dimensionality'' induces severe exploration inefficiency and training instability in expansive action spaces. Consequently, recent high-throughput paradigms have largely converged on deterministic policy gradients combined with massive parallel simulation. We challenge this compromise with FastDSAC, a framework that effectively unlocks the potential of maximum entropy stochastic policies for complex continuous control. We introduce Dimension-wise Entropy Modulation (DEM) to dynamically redistribute the exploration budget and enforce diversity, alongside a continuous distributional critic tailored to ensure value fidelity and mitigate high-dimensional value overestimation. Extensive evaluations on HumanoidBench and other continuous control tasks demonstrate that rigorously designed stochastic policies can consistently match or outperform deterministic baselines, achieving notable gains of 180\% and 400\% on the challenging \textit{Basketball} and \textit{Balance Hard} tasks.

Executive Summary

This article proposes FastDSAC, a framework that leverages maximum entropy reinforcement learning (RL) for high-dimensional humanoid control tasks. By introducing Dimension-wise Entropy Modulation (DEM) and a continuous distributional critic, FastDSAC enables the use of stochastic policies, achieving significant performance gains on challenging tasks such as Basketball and Balance Hard. The authors argue that their approach effectively addresses the 'curse of dimensionality' and provides a promising alternative to deterministic policy gradients. The framework's ability to match or outperform deterministic baselines on various continuous control tasks demonstrates its potential in overcoming the exploration inefficiency and training instability associated with high-dimensional action spaces. The article contributes to the development of more robust and efficient RL methods for complex control tasks.

Key Points

  • ▸ FastDSAC framework leverages maximum entropy RL for high-dimensional humanoid control tasks
  • ▸ Dimension-wise Entropy Modulation (DEM) and continuous distributional critic are introduced
  • ▸ Significant performance gains achieved on challenging tasks, including Basketball and Balance Hard

Merits

Strength in addressing exploration inefficiency

FastDSAC effectively addresses the 'curse of dimensionality' and provides a promising alternative to deterministic policy gradients, enabling the use of stochastic policies in high-dimensional action spaces.

Robustness and efficiency in complex control tasks

The framework's ability to match or outperform deterministic baselines on various continuous control tasks demonstrates its potential in overcoming exploration inefficiency and training instability.

Demerits

Potential computational overhead

The introduction of DEM and a continuous distributional critic may increase computational complexity, potentially affecting the framework's scalability and efficiency in real-world applications.

Limited evaluation on real-world humanoid control tasks

The article primarily focuses on simulated tasks, and further evaluation on real-world humanoid control tasks would be necessary to fully assess the framework's practicality and robustness.

Expert Commentary

The article presents a significant contribution to the field of RL, particularly in addressing the challenges of high-dimensional humanoid control tasks. The introduction of DEM and a continuous distributional critic demonstrates a nuanced understanding of the exploration-exploitation trade-off and its implications on RL performance. However, the potential computational overhead and limited evaluation on real-world tasks are notable concerns. Further research should focus on addressing these limitations and exploring the framework's scalability and practicality in real-world applications.

Recommendations

  • ✓ Future research should focus on addressing the computational overhead and scalability of the framework
  • ✓ Evaluation on real-world humanoid control tasks should be conducted to assess the framework's practicality and robustness

Sources

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