6d40ad1650
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
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Actions space: Discrete
References: Playing Atari with Deep Reinforcement Learning
Network Structure
Algorithm Description
Training the network
Sample a batch of transitions from the replay buffer.
Using the next states from the sampled batch, run the target network to calculate the Q values for each of the actions Q(st + 1, a), and keep only the maximum value for each state.
In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss), use the current states from the sampled batch, and run the online network to get the current Q values predictions. Set those values as the targets for the actions that were not actually played.
For each action that was played, use the following equation for calculating the targets of the network: $$ y_t=r(s_t,a_t)+γcdot max_a {Q(s_{t+1},a)} $$ yt = r(st, at) + γ⋅maxaQ(st + 1)
Finally, train the online network using the current states as inputs, and with the aforementioned targets.
Once in every few thousand steps, copy the weights from the online network to the target network.