Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Last update: Nov 22, 2022

Related tags

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

Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

UFT - Universal File Transfer With Python

PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Learning with Nonignorable Nonresponses‘

Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

Lorien: A Unified Infrastructure for Efficient Deep Learning Workloads Delivery

Binary Stochastic Neurons in PyTorch

Cards Against Humanity AI

This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

History Aware Multimodal Transformer for Vision-and-Language Navigation

Repositório criado para abrigar os notebooks com a listas de exercícios propostos pelo professor Gustavo Guanabara do canal Curso em Vídeo do YouTube durante o Curso de Python 3

Facial recognition project

Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches

Virtual hand gesture mouse using a webcam

[CoRL 2021] A robotics benchmark for cross-embodiment imitation.

Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

Intelligent Video Analytics toolkit based on different inference backends.

EfficientNetV2 implementation using PyTorch

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.