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

Researchers introduce FastDSAC, a new framework that successfully applies Maximum Entropy Reinforcement Learning to high-dimensional humanoid control tasks. The system uses Dimension-wise Entropy Modulation and continuous distributional critics to achieve 180% and 400% performance gains on challenging control tasks compared to deterministic methods.

• →FastDSAC overcomes the curse of dimensionality that has limited Maximum Entropy RL in complex continuous control tasks.
• →The framework introduces Dimension-wise Entropy Modulation to dynamically redistribute exploration budgets across action dimensions.
• →A continuous distributional critic is implemented to maintain value accuracy and prevent overestimation in high-dimensional spaces.
• →Testing on HumanoidBench shows stochastic policies can match or exceed deterministic baseline performance.
• →Notable performance improvements of 180% and 400% were achieved on Basketball and Balance Hard humanoid control tasks respectively.