Researchers introduce ReMax Actor-Critic (ReMAC), extending retry-based policy gradient methods from discrete to continuous action spaces. The approach uses pathwise derivative estimators to optimize pass@K and max@K objectives, promoting exploration through policy-gradient landscape reshaping rather than explicit entropy bonuses, achieving performance comparable to SAC.
ReMAC addresses a fundamental challenge in reinforcement learning: balancing exploration and exploitation without relying on hand-tuned entropy regularization. The research extends prior work on discrete retry objectives to the more complex continuous action domain, which is critical for robotics and control applications. By leveraging retry-based objectives that select the best outcome from multiple trajectory samples, the algorithm naturally encourages exploration through an elegant mechanism—reshaping how gradients flow through the policy network rather than artificially inflating rewards for random actions.
The technical contribution centers on understanding how ReMax alters the optimization landscape in two distinct ways: directionally biasing policy updates toward higher entropy solutions, and magnitudinally dampening gradients to slow convergence. This gradient damping effect reveals an interesting interaction with Adam optimizer's adaptive normalization, where the algorithm's numerical stabilization parameter becomes crucial for performance. The theoretical insight that deterministic rewards can still produce stochastic exploration through objective design represents a meaningful advance in understanding policy gradient dynamics.
For the machine learning and robotics communities, this work offers practical value by eliminating the need for careful entropy coefficient tuning, a common hyperparameter sensitivity in actor-critic methods like SAC. The empirical results demonstrate comparable performance to established baselines while maintaining simpler algorithmic requirements. This contribution strengthens the foundation for building more sample-efficient and stable reinforcement learning systems, particularly relevant for continuous control tasks in robotics, autonomous systems, and simulation-based optimization where exploration remains a critical bottleneck.
- →ReMAC extends retry-based policy gradients to continuous action spaces using pathwise derivative estimators
- →The algorithm promotes exploration by reshaping policy-gradient landscape without explicit entropy bonuses
- →Deterministic rewards can still produce stochastic exploration through objective function design
- →Adam optimizer's numerical stabilization parameter significantly affects gradient damping mitigation
- →ReMAC achieves comparable performance to SAC while simplifying hyperparameter requirements