metadata
tags:
- InvertedPendulum-v2
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: InvertedPendulum-v2
type: InvertedPendulum-v2
metrics:
- type: mean_reward
value: 1000.00 +/- 0.00
name: mean_reward
verified: false
(CleanRL) TD3 Agent Playing InvertedPendulum-v2
This is a trained model of a TD3 agent playing InvertedPendulum-v2. 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[td3_continuous_action_jax]"
python -m cleanrl_utils.enjoy --exp-name td3_continuous_action_jax --env-id InvertedPendulum-v2
Please refer to the documentation for more detail.
Command to reproduce the training
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v2-td3_continuous_action_jax-seed1/raw/main/td3_continuous_action_jax.py
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v2-td3_continuous_action_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v2-td3_continuous_action_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python td3_continuous_action_jax.py --track --capture-video --env-id InvertedPendulum-v2 --seed 1 --save-model --upload-model --hf-entity cleanrl
Hyperparameters
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'env_id': 'InvertedPendulum-v2',
'exp_name': 'td3_continuous_action_jax',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'policy_noise': 0.2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}