Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms

arXiv:2404.10645v1 Announce Type: new
Abstract: Distributed Distributional DrQ is a model-free and off-policy RL algorithm for continuous control tasks based on the state and observation of the agent, which is an actor-critic method with the data-augmentation and the distributional perspective of critic value function. Aim to learn to control the agent and master some tasks in a high-dimensional continuous space. DrQ-v2 uses DDPG as the backbone and achieves out-performance in various continuous control tasks. Here Distributed Distributional DrQ uses Distributed …

abstract actor actor-critic agent aim algorithm algorithms arxiv augmentation continuous control cs.ai cs.lg cs.ro data data-augmentation distributed free function learn observation perspective policy reinforcement reinforcement learning state tasks type value