metadata
env_name: Walker2d-v5
tags:
- Walker2d-v5
- sac
- reinforcement-learning
- custom-implementation
- policy-gradient
- pytorch
- ddpg
model-index:
- name: SAC-Walker2dV5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v5
type: Walker2d-v5
metrics:
- type: mean_reward
value: 4150.91 +/- 823.47
name: mean_reward
verified: false
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.