Implement the actor-critic methods

Zeta36 / Asynchronous-Methods-for-Deep-Reinforcement-Learning

Using a paper from Google DeepMind I've developed a new version of the DQN using threads exploration instead of memory replay as explain in here: http://arxiv.org/pdf/1602.01783v1.pdf I used the one-step-Q-learning pseudocode, and now we can train the Pong game in less than 20 hours and without any GPU or network distribution.

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Hello, In the asynchronous dqn paper, they also described an on policy method, the advantage actor-critic (A3C), which achieved better results than others, do you currently have any plan to include this method in this repo as well? Because I am working off this repo as a starting point, and attempt to reproduce the results of the A3C method on the continuous action domain, but I am still trying to figure out the network model they used in the physical state case when apply to Mojoco, and how the policy gradient is accumulated.

Zeta36 / Asynchronous-Methods-for-Deep-Reinforcement-Learning

Implement the actor-critic methods #1