Comments (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
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Related Issues (16)
- expert datasets HOT 4
- Getting missing args error running train_iq.py examples from run_offline.sh HOT 2
- Critic function is diverging while using SAC HOT 17
- Question regarding iq_loss implementation HOT 2
- Divergence Issue
- Poor performance on robosuite tasks
- Issue on robosuite tasks
- Offline Learning without access to environment.
- Issue on reproduce MuJoCo results HOT 5
- How to judge the convergence HOT 10
- Issue on reproduce MuJoCo results-HalfCheetah-v2 HOT 14
- Code for gridworld experiments HOT 3
- Issue on Ant-v2 expertd data and Humanoid-v2 random seed Experiments HOT 1
- Config for expert generation
- Pseudocode and questions
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