Where-to-Learn: Analytical Policy Gradient Directed Exploration for On-Policy Robotic Reinforcement Learning

arXiv cs.RO / 3/31/2026

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Key Points

The paper tackles the challenge of efficient exploration in on-policy reinforcement learning for robotics, where agents must discover high-reward trajectories without wasting interactions.
Instead of relying on generic exploration bonuses (e.g., maximizing policy entropy or encouraging novel state visitation), it proposes task-aware directed exploration guided by analytical policy gradients.
The method leverages a differentiable dynamics model to compute policy-gradient guidance, using physics/trajectory structure to steer the agent toward promising high-value regions.
The goal is to accelerate and improve policy learning quality by combining on-policy training with model-based, physics-guided exploration signals.
Overall, it presents a research idea aimed at improving sample efficiency and exploration effectiveness for robotic control using gradient-informed guidance from differentiable dynamics.

Abstract

On-policy reinforcement learning (RL) algorithms have demonstrated great potential in robotic control, where effective exploration is crucial for efficient and high-quality policy learning. However, how to encourage the agent to explore the better trajectories efficiently remains a challenge. Most existing methods incentivize exploration by maximizing the policy entropy or encouraging novel state visiting regardless of the potential state value. We propose a new form of directed exploration that uses analytical policy gradients from a differentiable dynamics model to inject task-aware, physics-guided guidance, thereby steering the agent towards high-reward regions for accelerated and more effective policy learning.