Comments (1)
Hi, I do not know how the rewards are calculated exactly, for that you will have to go through the code of roboschool env (for example: link).
Although rewards are important for RL, most of the RL research is based on standard benchmarks and abstract away the reward function as an black box function that needs to be optimized.
from ppo-pytorch.
Related Issues (20)
- policy.eval() after load_state_dict() HOT 1
- How are you ensuring that actions are in range of (-1,1) after sampling in continuous action HOT 1
- How to improve the performance based on your code? HOT 1
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- About environment configuration HOT 2
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- error HOT 5
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- question
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from ppo-pytorch.