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@ -102,28 +102,78 @@ Please see `fast_td3/hyperparams.py` for information regarding hyperparameters!
```bash
conda activate fasttd3_hb
# FastTD3
python fast_td3/train.py --env_name h1hand-hurdle-v0 --exp_name FastTD3 --render_interval 5000 --seed 1
python fast_td3/train.py \
--env_name h1hand-hurdle-v0 \
--exp_name FastTD3 \
--render_interval 5000 \
--seed 1
# FastTD3 + SimbaV2
python fast_td3/train.py --env_name h1hand-hurdle-v0 --exp_name FastTD3 --render_interval 5000 --agent fasttd3_simbav2 --batch_size 8192 --critic_learning_rate_end 3e-5 --actor_learning_rate_end 3e-5 --weight_decay 0.0 --critic_hidden_dim 512 --critic_num_blocks 2 --actor_hidden_dim 256 --actor_num_blocks 1 --seed 1
python fast_td3/train.py \
--env_name h1hand-hurdle-v0 \
--exp_name FastTD3 \
--render_interval 5000 \
--agent fasttd3_simbav2 \
--batch_size 8192 \
--critic_learning_rate_end 3e-5 \
--actor_learning_rate_end 3e-5 \
--weight_decay 0.0 \
--critic_hidden_dim 512 \
--critic_num_blocks 2 \
--actor_hidden_dim 256 \
--actor_num_blocks 1 \
--seed 1
```
### MuJoCo Playground Experiments
```bash
conda activate fasttd3_playground
# FastTD3
python fast_td3/train.py --env_name T1JoystickFlatTerrain --exp_name FastTD3 --render_interval 5000 --seed 1
python fast_td3/train.py --env_name G1JoystickFlatTerrain --exp_name FastTD3 --render_interval 5000 --seed 1
python fast_td3/train.py \
--env_name T1JoystickFlatTerrain \
--exp_name FastTD3 \
--render_interval 5000 \
--seed 1
# FastTD3 + SimbaV2
python fast_td3/train.py --env_name T1JoystickFlatTerrain --exp_name FastTD3 --render_interval 5000 --agent fasttd3_simbav2 --batch_size 8192 --critic_learning_rate_end 3e-5 --actor_learning_rate_end 3e-5 --weight_decay 0.0 --critic_hidden_dim 512 --critic_num_blocks 2 --actor_hidden_dim 256 --actor_num_blocks 1 --seed 1
python fast_td3/train.py \
--env_name T1JoystickFlatTerrain \
--exp_name FastTD3 \
--render_interval 5000 \
--agent fasttd3_simbav2 \
--batch_size 8192 \
--critic_learning_rate_end 3e-5 \
--actor_learning_rate_end 3e-5 \
--weight_decay 0.0 \
--critic_hidden_dim 512 \
--critic_num_blocks 2 \
--actor_hidden_dim 256 \
--actor_num_blocks 1 \
--seed 1
```
### IsaacLab Experiments
```bash
conda activate fasttd3_isaaclab
# FastTD3
python fast_td3/train.py --env_name Isaac-Velocity-Flat-G1-v0 --exp_name FastTD3 --render_interval 0 --seed 1
python fast_td3/train.py \
--env_name Isaac-Velocity-Flat-G1-v0 \
--exp_name FastTD3 \
--render_interval 0 \
--seed 1
# FastTD3 + SimbaV2
python fast_td3/train.py --env_name Isaac-Repose-Cube-Allegro-Direct-v0 --exp_name FastTD3 --render_interval 0 --agent fasttd3_simbav2 --batch_size 8192 --critic_learning_rate_end 3e-5 --actor_learning_rate_end 3e-5 --weight_decay 0.0 --critic_hidden_dim 512 --critic_num_blocks 2 --actor_hidden_dim 256 --actor_num_blocks 1 --seed 1
python fast_td3/train.py \
--env_name Isaac-Repose-Cube-Allegro-Direct-v0 \
--exp_name FastTD3 \
--render_interval 0 \
--agent fasttd3_simbav2 \
--batch_size 8192 \
--critic_learning_rate_end 3e-5 \
--actor_learning_rate_end 3e-5 \
--weight_decay 0.0 \
--critic_hidden_dim 512 \
--critic_num_blocks 2 \
--actor_hidden_dim 256 \
--actor_num_blocks 1 \
--seed 1
```
**Quick note:** For boolean-based arguments, you can set them to False by adding `no_` in front each argument, for instance, if you want to disable Clipped Q Learning, you can specify `--no_use_cdq` in your command.
@ -192,6 +242,16 @@ We would like to thank people who have helped throughout the project:
}
```
### SimbaV2
```bibtex
@article{lee2025hyperspherical,
title={Hyperspherical normalization for scalable deep reinforcement learning},
author={Lee, Hojoon and Lee, Youngdo and Seno, Takuma and Kim, Donghu and Stone, Peter and Choo, Jaegul},
journal={arXiv preprint arXiv:2502.15280},
year={2025}
}
```
### LeanRL
Following the [LeanRL](https://github.com/pytorch-labs/LeanRL)'s recommendation, we put CleanRL's bibtex here:
@ -278,4 +338,3 @@ Following the [LeanRL](https://github.com/pytorch-labs/LeanRL)'s recommendation,
url = "https://arthshukla.substack.com/p/speeding-up-sac-with-massively-parallel"
}
```