Eugene Vinitsky
TalkRL: The Reinforcement Learning Podcast
Exploring PPO in Multi-Agent Learning
This chapter examines a new study on the effectiveness of Proximal Policy Optimization (PPO) in cooperative multi-agent environments, showcasing its surprising performance against off-policy methods. The discussion covers key differences in the application of PPO for multi-agent scenarios and highlights the role of simulators in enhancing training and safety in real-world implementations.
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