Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Primary Area: reinforcement learning
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Keywords: Reinforcement Learning, Quality Diversity, Robotics, Machine Learning, Evolution Strategies
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TL;DR: We present a novel QD-RL method that leverages on-policy RL and Differentiable Quality Diversity to discover a variety of high performing locomtion gaits on the challenging mujoco environment, including, for the first time in QD-RL, humanoid.
Abstract: Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning. Quality Diversity Reinforcement Learning (QD-RL) is an emerging research area that blends the best aspects of both fields – Quality Diversity (QD) provides a principled form of exploration and produces collections of behaviorally diverse agents, while Reinforcement Learning (RL) provides a powerful performance improvement operator enabling generalization across tasks and dynamic environments. Existing QD-RL approaches have been constrained to sample efficient, deterministic off- policy RL algorithms and/or evolution strategies and struggle with highly stochastic environments. In this work, we, for the first time, adapt on-policy RL, specifically Proximal Policy Optimization (PPO), to the Differentiable Quality Diversity (DQD) framework and propose several changes that enable efficient optimization and discovery of novel skills on high-dimensional, stochastic robotics tasks. Our new algorithm, Proximal Policy Gradient Arborescence (PPGA), achieves state-of- the-art results, including a 4x improvement in best reward over baselines on the challenging humanoid domain.
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Submission Number: 8192