I am new to Reinforcement Learning. I am trying to train PPO agent for citylearn. The goal is to lower two environmental variables from observations. The default reward function is
reward = (variable1 + variable2)*-1
The agent is providing actions with values that are close to -1 for higher iterations. Instead of attempting to improve the environment, it only tries to avoid high negative rewards. I've tried normalizing observations and rewards, but it's still been ineffective.
Is there a reward function that would encourage exploration rather than the same behavior?