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Deep Reinforcement Learning Hands-On by Maxim Lapan

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The Actor-Critic (A2C) method

The first method that we'll apply to our walking robot problem is A2C, which we experimented with in part three of the book. This choice of method is quite obvious, as A2C is very easy to adapt to the continuous action domain. As a quick refresher, A2C's idea is to estimate the gradient of our policy as

The Actor-Critic (A2C) method

. The The Actor-Critic (A2C) method policy is supposed to provide to us the probability distribution of actions given the observed state. The quantity is called a critic, equals to the value of the state and is trained using the Mean Square Error ...

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