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Div99 avatar Div99 commented on May 26, 2024 1

Earlier in our experiments, we were using rewards as gamma * V(s') - Q(s, a) i.e. the negation of how it's defined in the paper. We changed this later, but the policy used for visualization was trained using this and so an extra negation is present.
(For training a new policy and evaluating this is not needed)

These are some hyperparams that should work well:
agent=sac agent.actor_lr=3e-05 agent.init_temperature=0.01 agent.learnable_temperature=False env=pointmaze_right method.loss=v0 method.regularize=True num_actor_updates=4 num_seed_steps=0 only_expert_states=True train.batch=256 train.use_target=True

from iq-learn.

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