Behind the Myth of Exploration in Policy Gradients

Policy-gradient algorithms are effective reinforcement learning methods for solving control problems with continuous state and action spaces. To compute near-optimal policies, it is essential in practice to include exploration terms in the learning objective. Although the effectiveness of these terms is usually justified by an intrinsic need to explore environments, we propose a novel analysis and distinguish two different implications of these techniques. First, they make it possible to smooth the learning objective and to eliminate local optima while preserving …

algorithms compute continuous control cs.lg environments exploration explore gradient intrinsic near policy policy-gradient practice reinforcement reinforcement learning spaces state stat.ml terms