Q-learning and Sarsa in grid environment for short-term vs long-term rewards

I created my custom, grid(7 by 7) environment to apply RL algorithms. I chose Q-learning and Sarsa, in particular.

The grid environment consists of 3 types of terminating states: states with negative reward(-100), state with maximum reward(100) and 2 states with half reward(50).

The main goal of training is for the agent to avoid states with negative rewards and to prefer long-term reward(100) over short-term half reward(50).

The trained agent works weirdly when the half-rewarded state is closer to the …

artificial environment learning