SAC Agent playing Walker2d-v5
This is a trained model of a SAC agent playing Walker2d-v5.
Usage
create the conda env in https://github.com/GeneHit/drl_practice
conda create -n drl python=3.10
conda activate drl
python -m pip install -r requirements.txt
play with full model
# load the full model
model = load_from_hub(repo_id="winkin119/SAC-Walker2dV5", filename="full_model.pt")
# Create the environment.
env = gym.make("Walker2d-v5")
state, _ = env.reset()
action = model.action(state)
...
There is also a state dict version of the model.
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Evaluation results
- mean_reward on Walker2d-v5self-reported4150.91 +/- 823.47