metadata
env_name: CartPole-v1
tags:
- CartPole-v1
- a3c with gae
- reinforcement-learning
- custom-implementation
- policy-gradient
- pytorch
- a3c
- gae
model-index:
- name: A3C-CartPoleV1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
A3C with GAE Agent playing CartPole-v1
This is a trained model of a A3C with GAE agent playing CartPole-v1.
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/A3C-CartPoleV1", filename="full_model.pt")
# Create the environment.
env = gym.make("CartPole-v1")
state, _ = env.reset()
action = model.action(state)
...
There is also a state dict version of the model, you can check the corresponding definition in the repo.