Where-to-Learn: Analytical Policy Gradient Directed Exploration for On-Policy Robotic Reinforcement Learning
For robotic control tasks, this method addresses the challenge of efficient exploration in on-policy RL by providing task-aware, physics-guided guidance.
The paper proposes a directed exploration method for on-policy robotic RL that uses analytical policy gradients from a differentiable dynamics model to guide the agent towards high-reward regions, improving sample efficiency and policy quality.
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.