Comments (3)
Tell me if I'm wrong, but by looking at that code:
make_vec_env(lambda: env, wrapper_class=bsuite.utils.gym_wrapper.GymFromDMEnv)
would work, no?
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Ah, sorry, mb. I was under the impression that make_vec_env
only took strings so loading bsuite env would be slightly more involved.
from stable-baselines3.
In fact, it expects an env class or a string (I updated my example)
from stable-baselines3.
Related Issues (20)
- [Bug]: EOFError after running for some steps HOT 1
- [Question] Saving PPO rollout buffer on GPU HOT 2
- Issue(HER with in SAC algorithm) HOT 2
- [Question] CheckpointCallback keep last K HOT 2
- [Bug]: Potential Bug in PPO? Clarification requested HOT 2
- Off policy algorithm policy_kwargs HOT 2
- [Feature Request] Enable predict to take tensor as input HOT 3
- [Question] policy gradient loss and explained variance very small (almost zero) from the training start? HOT 2
- [Question] Discontinuous reward training curve HOT 4
- [Bug]: if learning_rate function uses special types, they can cause torch.load to fail when weights_only=True HOT 4
- [Question] How to avoid SAC to stuck in local minima HOT 1
- Scalability HOT 2
- [Bug]: Scaling Environment HOT 9
- Scaling Environment HOT 6
- Discrepancy between Observations Sampled from Gym Env and Replay Buffer HOT 3
- [Bug]: Load Trained Policy HOT 8
- [Bug]: evaluate_policy called multiple times vor vectorized environments HOT 5
- Handing mission space in Babyai env HOT 6
- ValueError: could not broadcast input array from shape (23,) into shape (27,) HOT 2
- SAC model not properly saved HOT 5
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