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lukasmolnar
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Jun 24, 2024
sheim
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Jul 2, 2024
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sheim
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sheim
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Overall looks good, I haven't checked in detail the GePPO implementation, is there something specific you want me to look at closely?
| activation="elu", | ||
| init_noise_std=1.0, | ||
| normalize_obs=True, | ||
| store_pik=False, |
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use readable variable names. What is "pik"?
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I think this would be easy enough to create a new actor class that inherits from the vanilla actor, what do you think?
learning/modules/utils/normalize.py
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| mean = input.mean(tuple(range(input.dim() - 1))) | ||
| var = input.var(tuple(range(input.dim() - 1))) | ||
| # TODO: check this, it got rid of NaN values in first iteration | ||
| dim = tuple(range(input.dim() - 1)) |
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| # Implementation based on GePPO repo: https://github.com/jqueeney/geppo | ||
| @torch.no_grad | ||
| def compute_gae_vtrace(data, gamma, lam, is_trunc, actor, critic, rec=False): |
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rule of thumb, don't abbreviate (rec --> recursive)
| offpol_ratio = torch.exp(log_prob_pik - batch["log_prob"]) | ||
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| advantages = batch["advantages"] | ||
| if self.normalize_advantages: |
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this is currently set to False, which surprises me, that seemed to be quite important in PPO...
| counter += 1 | ||
| self.mean_surrogate_loss /= counter | ||
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| # Compute TV, add to self for logging |
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