Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Last update: Nov 22, 2022

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Overview

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

This is the official repository for Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning. We provide the commands to run the PETS and PlaNet experiments included in the paper. This repository is made minimal for ease of experimentation.

Installations

This repository requires Python (3.6), Pytorch (version 1.3 or above) run the following command to create a conda environment (tested using CUDA10.2):

conda env create -f environment.yml

Experiments

To run the PETS experiments on the HalfCheetah environment used in our ablation study, run:

cd cap-pets

CAP

python cap-pets/run_cap_pets.py --algo cem --env HalfCheetah-v3 --cost_lim 152 \
--cost_constrained --penalize_uncertainty --learn_kappa --seed 1

CAP with fixed kappa

python cap-pets/run_cap_pets.py --algo cem --env HalfCheetah-v3 --cost_lim 152 \
--cost_constrained --penalize_uncertainty --kappa 1.0 --seed 1

CCEM

python cap-pets/run_cap_pets.py --algo cem --env HalfCheetah-v3 --cost_lim 152 \
--cost_constrained --seed 1

CEM

python cap-pets/run_cap_pets.py --algo cem --env HalfCheetah-v3 --cost_lim 152 \
--seed 1

The commands for the PlaNet experiment on the CarRacing environment are:

CAP

python cap-planet/run_cap_planet.py --env CarRacingSkiddingConstrained-v0 \
--cost-limit 0 --binary-cost \
--cost-constrained --penalize-uncertainty \
--learn-kappa --penalty-kappa 0.1 \
--id CarRacing-cap --seed 1

CAP with fixed kappa

python cap-planet/run_cap_planet.py --env CarRacingSkiddingConstrained-v0 \
--cost-limit 0 --binary-cost \
--cost-constrained --penalize-uncertainty \
--penalty-kappa 1.0 \
--id CarRacing-kappa1 --seed 1

CCEM

python cap-planet/run_cap_planet.py --env CarRacingSkiddingConstrained-v0 \
--cost-limit 0 --binary-cost \
--cost-constrained \
--id CarRacing-ccem --seed 1

CEM

python cap-planet/run_cap_planet.py --env CarRacingSkiddingConstrained-v0 \
--cost-limit 0 --binary-cost \
--id CarRacing-cem --seed 1

Contact

If you have any questions regarding the code or paper, feel free to contact [email protected] or open an issue on this repository.

Acknowledgement

This repository contains code adapted from the following repositories: PETS and PlaNet. We thank the authors and contributors for open-sourcing their code.

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Related tags

Overview

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Installations

Experiments

To run the PETS experiments on the HalfCheetah environment used in our ablation study, run:

The commands for the PlaNet experiment on the CarRacing environment are:

Contact

Acknowledgement

Owner

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Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Related tags

Overview

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Installations

Experiments

To run the PETS experiments on the HalfCheetah environment used in our ablation study, run:

The commands for the PlaNet experiment on the CarRacing environment are:

Contact

Acknowledgement

Owner

The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

Tensorflow Implementation of Pixel Transposed Convolutional Networks (PixelTCN and PixelTCL)

基于Paddle框架的arcface复现

U-Time: A Fully Convolutional Network for Time Series Segmentation

Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

Implementation of the CVPR 2021 paper "Online Multiple Object Tracking with Cross-Task Synergy"

Realistic lighting in ursina!

Pytorch code for "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks".

CenterNet:Objects as Points目标检测模型在Pytorch当中的实现

Adversarial Self-Defense for Cycle-Consistent GANs

Human4D Dataset tools for processing and visualization

MWPToolkit is a PyTorch-based toolkit for Math Word Problem (MWP) solving.

Official pytorch code for SSC-GAN: Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation(ICCV 2021)

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

ICCV2021 Oral SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

⚖️🔁🔮🕵️‍♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.

Implementation of Hourglass Transformer, in Pytorch, from Google and OpenAI

An automated algorithm to extract the linear blend skinning (LBS) from a set of example poses

Continual Learning of Long Topic Sequences in Neural Information Retrieval

⚖️🔁🔮🕵️‍♂️🦹🖼️ Code for Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances paper.