I would like to train a neural network (NN) so that it learns the policy and value function for my agent.
Since I am using reinforcement learning and do not want to prefer certain actions in certain states at the beginning of the learning, ideally, my NN should be initialized in a way that it predicts a uniform policy for all of the actions in every state and then during training, it will adjust its weights based on the observations.
The idea for this weight initialization is to speed up the training process by not "delearning" random initial policy that can be off by quite some margin and to also guarantee equal exploration from every state straight from the beginning.
I would like to ask two questions about this topic:
- Is this a good idea?
- Are there any available tools for achieving this?