Reinforcement Learning with Learned Fourier Features
State-space Soft Actor-Critic Experiments
Move to the state-SAC-LFF repository.
cd state-SAC-LFF
To install the dependencies, use the provided environment.yml file
conda env create -f environment.yml
To run an experiment, the template for MLP and LFF experiments, respectively, are:
python main.py --policy PytorchSAC --env dm.quadruped.run --start_timesteps 5000 --hidden_dim 1024 --batch_size 1024 --n_hidden 3
python main.py --policy PytorchSAC --env dm.quadruped.run --start_timesteps 5000 --hidden_dim 1024 --batch_size 1024 --n_hidden 2 \
--network_class FourierMLP --sigma 0.001 --fourier_dim 1024 --train_B --concatenate_fourier
The only thing that changes between the baseline is the number of hidden layers (we reduce by 1 to keep parameter count roughly the same), the network_class, the fourier_dim, sigma, train_B, and concatenate_fourier.
Image-space Soft Actor-Critic Experiments
Move to the image-SAC-LFF repository.
cd image-SAC-LFF
Install RAD dependencies:
conda env create -f conda_env.yml
To run an experiment, the template for CNN and CNN+LFF experiments, respectively, are:
python train.py --domain_name hopper --task_name hop --encoder_type fourier_pixel --action_repeat 4 \
--num_eval_episodes 10 \--pre_transform_image_size 100 --image_size 84 --agent rad_sac \
--frame_stack 3 --data_augs crop --critic_lr 1e-3 --actor_lr 1e-3 --eval_freq 10000 --batch_size 128 \
--num_train_steps 1000000 --fourier_dim 128 --sigma 0.1 --train_B --concatenate_fourier
python train.py --domain_name hopper --task_name hop --encoder_type fair_pixel --action_repeat 4 \
--num_eval_episodes 10 \--pre_transform_image_size 100 --image_size 84 --agent rad_sac \
--frame_stack 3 --data_augs crop --critic_lr 1e-3 --actor_lr 1e-3 --eval_freq 10000 --batch_size 128 \
--num_train_steps 1000000
Proximal Policy Optimization Experiments
Move to the state-PPO-LFF repository.
cd pytorch-a2c-ppo-acktr-gail
Install PPO dependencies:
conda env create -f environment.yml
To run an experiment, the template for MLP and LFF experiments, respectively, are:
python main.py --env-name Hopper-v2 --algo ppo --use-gae --log-interval 1 --num-steps 2048 --num-processes 1 \
--lr 3e-4 --entropy-coef 0 --value-loss-coef 0.5 --ppo-epoch 10 --num-mini-batch 32 --gamma 0.99 \
--gae-lambda 0.95 --num-env-steps 1000000 --use-linear-lr-decay --use-proper-time-limits \
--hidden_dim 256 --network_class MLP --n_hidden 2 --seed 10
python main.py --env-name Hopper-v2 --algo ppo --use-gae --log-interval 1 --num-steps 2048 --num-processes 1 \
--lr 3e-4 --entropy-coef 0 --value-loss-coef 0.5 --ppo-epoch 10 --num-mini-batch 32 --gamma 0.99 \
--gae-lambda 0.95 --num-env-steps 1000000 --use-linear-lr-decay --use-proper-time-limits \
--hidden_dim 256 --network_class FourierMLP --n_hidden 2 --sigma 0.01 --fourier_dim 64 \
--concatenate_fourier --train_B --seed 10
Acknowledgements
We built the state-based SAC codebase off the TD3 repo by Fujimoto et al. We especially appreciated its lightweight bare-bones training loop. For the state-based SAC algorithm implementation and hyperparameters, we used this PyTorch SAC repo by Yarats and Kostrikov. For the SAC+RAD image-based experiments, we used the authors' implementation. Finally, we built off this PPO codebase by Ilya Kostrikov.
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