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

Character Grounding and Re-Identification in Story of Videos and Text Descriptions

Official Implementation of "DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization."

Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies

A DCGAN to generate anime faces using custom mined dataset

Official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

Used to record WKU's utility bills on a regular basis.

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

Implementation of Graph Convolutional Networks in TensorFlow

Keras udrl - Keras implementation of Upside Down Reinforcement Learning

New approach to benchmark VQA models

Text Extraction Formulation + Feedback Loop for state-of-the-art WSD (EMNLP 2021)

Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.

A tool for making map images from OpenTTD save games

Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

网络协议2天集训

PyTorch implementations of deep reinforcement learning algorithms and environments

Tracking Pipeline helps you to solve the tracking problem more easily

Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Robust Consistent Video Depth Estimation