Code for the paper "Functional Regularization for Reinforcement Learning via Learned Fourier Features"

Last update: Nov 11, 2022

Related tags

Overview

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.

Code for the paper "Functional Regularization for Reinforcement Learning via Learned Fourier Features"

Related tags

Overview

Reinforcement Learning with Learned Fourier Features

State-space Soft Actor-Critic Experiments

Image-space Soft Actor-Critic Experiments

Proximal Policy Optimization Experiments

Acknowledgements

Owner

Alex Li

[NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

GNN-based Recommendation Benchma

Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"

A PyTorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Fastshap: A fast, approximate shap kernel

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Universal Probability Distributions with Optimal Transport and Convex Optimization

Offcial repository for the IEEE ICRA 2021 paper Auto-Tuned Sim-to-Real Transfer.

FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

Code for the paper "Improved Techniques for Training GANs"

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

Educational API for 3D Vision using pose to control carton.

performing moving objects segmentation using image processing techniques with opencv and numpy

Code for HodgeNet: Learning Spectral Geometry on Triangle Meshes, in SIGGRAPH 2021.

PyTorch implementation of Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network