Abstract:Reinforcement learning and deep reinforcement learning, as a research hotspot in the field of machine learning, have been widely used in our daily life. In this field, game is playing an extremely important role in the developing of reinforcement learning algorithms. Based on the Tennis environment built by Unity ML Agents, this paper used three algorithms, Proximal Policy Optimization (PPO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG) and Soft Actor-Critic (SAC), combined with PyTorch framework, … Show more
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