I am a bit confused regarding rewards in reinforcement learning. In my quite simple environment, where the agent has to find it's way to a target and kill it, the agent has control over heading direction, speed and firing. I have given the following rewards:
- killing: 100
- going outside boundary: -5
for each time step:
- shaped reward
f(dist, angle)which lies in [0,1] depending on distance to target and direction.
I am using PPO with Ray RLlib and somehow I don't get the desired behaviour. Sometimes the agent finds it's way to the target, but often not. My parameters are set as:
- fully connected hidden layers: [128, 128, 64]
- train batch size: 1000
- time horizon: 100 (should satisfy)
- learning rate: 0.0005
I don't know if I can to it like that, but I am wondering if my rewards make sense ?