[Question] exp_manager reward and GAE discount factors

[edmund735]

❓ Question

Hi,

I noticed that the algorithm discount factor and reward discount factor are set to be the same in lines 365-367 in rl_zoo3/exp_manager.py

# Use the same discount factor as for the algorithm. if "gamma" in hyperparams: self.normalize_kwargs["gamma"] = hyperparams["gamma"]

For PPO, does this mean the discount factor used for GAE is the same as the reward discount factor? I'm currently training an environment with PPO that is episodic (episodes always reach a termination state by 100 time steps, so there is never truncation) and I'd like to have a reward discount factor of 1. In this case, if I want to do hyperparameter tuning for the GAE discount factor, should I remove this line so that the VecNormalize object created uses a discount factor of 1 (different from the GAE discount factor)? Also, why are the discount factors matched only if normalize =True (isn't it possible that you still have a reward discount factor without normalization)? I read #64 ("gamma is the only one we override automatically for correctness (and only if present in the hyperparameters)" and don't think I understand what "correctness" means in this case. Any further explanation would be very helpful.

Thanks!

Checklist

• I have checked that there is no similar issue in the repo
• I have read the SB3 documentation
• I have read the RL Zoo documentation
• If code there is, it is minimal and working
• If code there is, it is formatted using the markdown code blocks for both code and stack traces.

[araffin]

For PPO, does this mean the discount factor used for GAE is the same as the reward discount factor?

I guess there is confusion between GAE and what that piece of code do.
This code snippet is only about VecNormalize and the way it normalizes the reward (there are many issues in SB2/SB3 repo about why it is like that).

GAE uses two hyperparameters, gamma the discount factor and gae_lambda which does the tradeoff between TD(0) and MC estimate.