metadata
tags:
- BeamRiderNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: C51
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRiderNoFrameskip-v4
type: BeamRiderNoFrameskip-v4
metrics:
- type: mean_reward
value: 5873.40 +/- 1897.78
name: mean_reward
verified: false
(CleanRL) C51 Agent Playing BeamRiderNoFrameskip-v4
This is a trained model of a C51 agent playing BeamRiderNoFrameskip-v4. The model was trained by using CleanRL and the most up-to-date training code can be found here.
Get Started
To use this model, please install the cleanrl
package with the following command:
pip install "cleanrl[c51_atari_jax]"
python -m cleanrl_utils.enjoy --exp-name c51_atari_jax --env-id BeamRiderNoFrameskip-v4
Please refer to the documentation for more detail.
Command to reproduce the training
curl -OL https://huggingface.co/kinalmehta/BeamRiderNoFrameskip-v4-c51_atari_jax-seed1/raw/main/c51_atari_jax.py
curl -OL https://huggingface.co/kinalmehta/BeamRiderNoFrameskip-v4-c51_atari_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/kinalmehta/BeamRiderNoFrameskip-v4-c51_atari_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python c51_atari_jax.py --save-model --upload-model --hf-entity kinalmehta --env-id BeamRiderNoFrameskip-v4
Hyperparameters
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'end_e': 0.01,
'env_id': 'BeamRiderNoFrameskip-v4',
'exp_name': 'c51_atari_jax',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'kinalmehta',
'learning_rate': 0.00025,
'learning_starts': 80000,
'n_atoms': 51,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 10000,
'total_timesteps': 10000000,
'track': False,
'train_frequency': 4,
'upload_model': True,
'v_max': 10,
'v_min': -10,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}