Policy Gradient Algorithms
Policy gradient algorithms are a type of reinforcement learning method used to train policies that map states to actions. The article provides an overview and updates on various policy gradient methods, including SAC, D4PG, TD3, SVPG, IMPALA, and PPG. These methods have been added over time as new research has emerged.
Policy gradient algorithms are a type of reinforcement learning method used to train policies that map states to actions. The article provides an overview and updates on various policy gradient methods, including SAC, D4PG, TD3, SVPG, IMPALA, and PPG. These methods have been added over time as new research has emerged.
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Why it matters: Understanding policy gradient algorithms is crucial for researchers in AI who work with reinforcement learning, as these methods are used to train policies that can learn from experience and improve their performance over time.
Source: https://lilianweng.github.io/posts/2018-04-08-policy-gradient/
This article was originally published at: https://lilianweng.github.io/posts/2018-04-08-policy-gradient/