Dynamic Entropy Tuning in Reinforcement Learning Low-Level Quadcopter Control: Stochasticity vs Determinism

Researchers compare dynamic entropy tuning in stochastic reinforcement learning policies versus deterministic policies for quadcopter control, finding that dynamic entropy adjustment in the Soft Actor-Critic algorithm prevents catastrophic forgetting and improves exploration efficiency compared to static entropy or purely deterministic approaches using TD3.

This research addresses a fundamental challenge in reinforcement learning: balancing exploration and exploitation during training while maintaining stable control performance. The study demonstrates that dynamic entropy tuning—adjusting the randomness of action selection during training—provides measurable advantages in low-level robotic control tasks. By comparing SAC with dynamic entropy against static entropy variants and TD3's deterministic approach, the researchers identify a critical sweet spot where controlled stochasticity during training leads to more robust policies.

The problem of catastrophic forgetting occurs when neural networks trained on new tasks forget previously learned knowledge, a significant issue in sequential control tasks. The dynamic entropy mechanism appears to mitigate this through continuous exploration that refreshes and stabilizes learned representations. This builds on years of RL research showing that pure determinism often converges to local optima while uncontrolled stochasticity introduces excessive noise.

For the robotics and autonomous systems industry, these findings have practical implications. Quadcopter control represents a challenging domain with continuous state-action spaces and real-time constraints. More reliable RL algorithms reduce the need for extensive manual tuning and sim-to-real transfer complications. The results suggest that practitioners should consider dynamic entropy approaches when deploying SAC-based controllers rather than static configurations.

Future work likely involves testing these principles on physical hardware and exploring whether dynamic entropy mechanisms transfer across different robot morphologies and tasks. The research contributes incremental but solid progress toward more autonomous, self-correcting robotic systems that maintain performance stability throughout extended training periods.

• →Dynamic entropy tuning in SAC prevents catastrophic forgetting compared to static entropy configurations in quadcopter control
• →Stochastic policies with dynamic entropy outperform purely deterministic TD3 policies despite executing deterministic actions
• →Dynamic entropy improves exploration efficiency during training, leading to more robust control policies
• →The approach addresses practical challenges in deploying RL algorithms to continuous control tasks
• →Results suggest practitioners should adopt dynamic entropy mechanisms in SAC implementations for robotic control