MBPO (paper: When to trust your model: Model-based policy optimization) in offline RL settings

Related tags

Deep Learning mbpo_pytorch_offline
Overview

offline-MBPO

This repository contains the code of a version of model-based RL algorithm MBPO, which is modified to perform in offline RL settings
Paper:When to trust your model: Model-based policy optimization
With much thanks, this code is based on Xingyu-Lin's easy-to-read pytorch implementation of MBPO

Requirements

See requirements.txt
The code depends on D4RL's environments and datasets
Only support hopper, walker, halfcheetah and ant environments right now (if you wish to evaluate in other environments, modify the termination function in predict_env.py)

Usage

Simply run

  
python main_mbpo.py --env_name=halfcheetah-medium-v0 --seed=1234

Or modify the script runalgo.sh, then

  
bash runalgo.sh
You might also like...
PyTorch implementation of Constrained Policy Optimization
PyTorch implementation of Constrained Policy Optimization

PyTorch implementation of Constrained Policy Optimization (CPO) This repository has a simple to understand and use implementation of CPO in PyTorch. A

Sapana Chaudhary 25 Dec 8, 2022
An implementation of the proximal policy optimization algorithm
An implementation of the proximal policy optimization algorithm

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 9, 2022
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,

GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan 70 Dec 18, 2022
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 3, 2023
library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

NLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unifi

Steven G. Johnson 1.4k Dec 25, 2022
Racing line optimization algorithm in python that uses Particle Swarm Optimization.
Racing line optimization algorithm in python that uses Particle Swarm Optimization.

Racing Line Optimization with PSO This repository contains a racing line optimization algorithm in python that uses Particle Swarm Optimization. Requi

Parsa Dahesh 6 Dec 14, 2022
A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.
A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

AMAZ3DSim AMAZ3DSim is a lightweight python-based 3D network multi-agent simulator. It uses a cell-based congestion model. It calculates risk, battery

Daniel Hirsch 13 Nov 4, 2022
Code for EMNLP 2021 main conference paper
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Text-AutoAugment (TAA) This repository contains the code for our paper Text AutoAugment: Learning Compositional Augmentation Policy for Text Classific

LancoPKU 105 Jan 3, 2023
Training code and evaluation benchmarks for the
Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.

Self-Supervised Policy Adaptation during Deployment PyTorch implementation of PAD and evaluation benchmarks from Self-Supervised Policy Adaptation dur

Nicklas Hansen 101 Nov 1, 2022
Owner
LxzGordon
UCAS PhD student.
LxzGordon
GitHub
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

pytorch-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out

Ilya Kostrikov 3k Jan 9, 2023
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

Ilya Kostrikov 3k Dec 31, 2022
A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

Overview This is a re-implementation of the model-based RL algorithm MBPO in pytorch as described in the following paper: When to Trust Your Model: Mo

Xingyu Lin 93 Jan 5, 2023
Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO)

V-MPO Simple code to demonstrate Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) in Pyt

Nugroho Dewantoro 9 Jun 6, 2022
RoMA: Robust Model Adaptation for Offline Model-based Optimization

RoMA: Robust Model Adaptation for Offline Model-based Optimization Implementation of RoMA: Robust Model Adaptation for Offline Model-based Optimizatio

null 9 Oct 31, 2022
'A C2C E-COMMERCE TRUST MODEL BASED ON REPUTATION' Python implementation

Project description A library providing functionalities to calculate reputation and degree of trust on C2C ecommerce platforms. The work is fully base

Davide Bigotti 2 Dec 14, 2022
Active Offline Policy Selection With Python

Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian

DeepMind 27 Oct 15, 2022
Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

null 3 Dec 2, 2022
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Alexis David Jacq 163 Dec 26, 2022
ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 9, 2022