A2C on Pong
In the previous chapter, you saw a (not very successful) attempt to solve our favorite Pong environment with policy gradient methods. Let's try it again with the actor-critic method at hand.
GAMMA = 0.99
LEARNING_RATE = 0.001
ENTROPY_BETA = 0.01
BATCH_SIZE = 128
NUM_ENVS = 50
REWARD_STEPS = 4
CLIP_GRAD = 0.1
We start, as usual, by defining hyperparameters (imports are omitted). These values are not tuned, as we will do this in the next section of this chapter. We have one new value here: CLIP_GRAD. This hyperparameter specifies the threshold for gradient clipping, which basically prevents our gradients from becoming too large at the optimization stage and pushing our policy too far. Clipping is implemented using the PyTorch functionality, but the idea is very simple: if the L2 norm of the gradient is larger than this hyperparameter, then the gradient vector is clipped to this value.
The REWARD_STEPS hyperparameter determines how many...
