I have read that in reinforcement learning, maximizing the entropy enables the policy to behave more randomly. My question comes in three parts:
(1) In the equation below in the cross-entropy term what does the dot • symbol stand for?
(2) if maximizing the entropy also makes the policy behave more randomly - then does that mean that it prevents training an optimum policy to convergence?
entropy = -tf.reduce_sum(policy * log_policy, 1, name="entropy")
However the policy is the output of the softmax and not the actual label as is usually the case for cross entropy. Is there a reason why the label (0 for move left, or 1 for move right) was not used.