This chapter explores the use of distributed reinforcement learning, which involves multiple agents running in parallel to interact with the environment to generate sample trajectories or transitions, and use samples to train the agent (e.g., to learn the optimal policy or value function). This approach offers several benefits over single-agent architectures, including faster convergence, better exploration, improved robustness, and increased scalability.
By running multiple agents in parallel, ...
