Description
Learn what RL is and how the algorithms help solve problems
Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
Dive deep into a range of value and policy gradient methods
Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning
Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
Get practical examples through the accompanying website
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