I want to create an AI which can play five-in-a-row/gomoku. I want to use reinforcement learning for this.
I use policy gradient method, namely REINFORCE, with baseline. For the value and policy function approximation, I use a neural network. It has convolutional and fully connected layers. All of the layers, except for the output, are shared. The policy's output layer has $8 \times 8=64$ (the size of the board) output unit and softmax on them. So it is stochastic.
But what if the network produces a very high probability for an invalid action? An invalid action is when the agent wants to check a square that has one
O in it. I think it can be stuck in that game state.
How should I handle this situation?
My guess is to use the actor-critic method. For an invalid action, we should give a negative reward and pass the turn to the opponent.