Deep reinforcement learning - how to deal with boundaries in action space
Deep reinforcement learning - how to deal with boundaries in action space I've built a custom reinforcement learning environment and agent which is similar to a labyrinth game. environment agent In labyrinth there're 5 possible actions: up, down, left, right, and stay. While if blocked, e.g. agent can't go up, then how do people design env and agent to simulate that? env agent To be specific, the agent is at current state s0 , and by definition taking actions of down, left, and right will change the state to some other values with an immediate reward (>0 if at the exit). One possible approach is when taking action up , the state will stay at s0 and the reward will be a large negative number. Ideally the agent will learn that and never go up again at this state. s0 up s0 up However, my agent seems not learning this. Instead, it still goes up . Another approach is to hard code the agent and the environment that the agent will not be able to perform the action up whe...