Retry Policy Gradients in Continuous Action Spaces

2026-06-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Retry Policy Gradients in Continuous Action Spaces introduces pathwise derivative estimators to extend the ReMax objective, previously used in discrete action spaces, to continuous action environments. This work demonstrates that ReMax can foster stochastic exploration even with deterministic rewards by significantly reshaping the policy-gradient landscape. Specifically, it alters gradient direction, biasing updates toward higher policy entropy, and modifies gradient magnitude, damping updates and slowing convergence. The authors show that Adam's adaptive normalization can mitigate this damping, depending on its numerical stabilization parameter. Empirically, this objective is instantiated as ReMax Actor-Critic (ReMAC), an off-policy actor-critic algorithm. Experiments indicate that ReMAC promotes higher policy entropy without explicit entropy regularization and achieves performance comparable to SAC.

Key takeaway

For AI Scientists developing continuous control agents, consider integrating ReMax Actor-Critic (ReMAC) into your reinforcement learning toolkit. This approach offers a robust method for promoting stochastic exploration and achieving higher policy entropy without requiring explicit entropy regularization. By understanding how ReMax reshapes policy gradients and how Adam's adaptive normalization can mitigate gradient damping, you can fine-tune its application to achieve performance comparable to SAC, potentially simplifying your exploration strategy.

Key insights

ReMax, extended to continuous action spaces, promotes stochastic exploration by reshaping policy gradients.

Principles

• Retry objectives can promote exploration without explicit bonuses.
• ReMax alters policy gradients toward higher entropy.
• Adam's normalization can counteract gradient damping.

Method

The article introduces pathwise derivative estimators for retry objectives to extend ReMax to continuous action spaces, then instantiates it as ReMAC, an off-policy actor-critic algorithm.

In practice

• Implement ReMAC for continuous control tasks.
• Explore retry objectives for enhanced exploration.
• Consider Adam's parameters to manage gradient damping.

Topics

• Reinforcement Learning
• Continuous Action Spaces
• ReMax Actor-Critic
• Policy Gradients
• Stochastic Exploration
• Adam Optimizer

Best for: Research Scientist, AI Scientist

Related on AIssential

• Retry Policy Gradients in Continuous Action Spaces — ReMax extends to continuous action spaces, promoting exploration by reshaping policy gradients without entropy bonuses.
• Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying — ReMax induces emergent stochastic exploration in RL by optimizing for expected maximum return over retries, eliminating explicit bonuses.
• Gradient-based Planning for World Models at Longer Horizons — GRASP enables robust, long-horizon planning with learned world models by re-engineering gradient flow and introducing state-level stochasticity.
• Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control — Exploiting reflection symmetry in state-based RL significantly improves sample efficiency and performance.
• Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning — TAO-RL stabilizes agentic reinforcement learning by filtering tool-use trajectories and guiding exploration with entropy for efficient LLM policy optimization.
• Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition — Second-order actor-critic methods can achieve stable, efficient updates by decomposing the policy Hessian.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.