Agent-Only's repositories
book-recommendation-system
a simple book recommendation application
BayesOpt_Attack
Targeted black-box adversarial attack using Bayesian Optimization
botorch
Bayesian optimization in PyTorch
ContinuousParetoMTL
[ICML 2020] PyTorch Code for "Efficient Continuous Pareto Exploration in Multi-Task Learning"
CrowdNav
[ICRA19] Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
DBRL
Dataset Batch(offline) Reinforcement Learning for recommender system
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
deep_rl
PyTorch implementations of Deep Reinforcement Learning algorithms (DQN, DDQN, A2C, VPG, TRPO, PPO, DDPG, TD3, SAC, ASAC, TAC, ATAC)
FNI-RL
This repo is the implementation of "Fear-Neuro-Inspired Reinforcement Learning for Safe Autonomous Driving".
MAAC
Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019
MOBO
constrained/unconstrained multi-objective bayesian optimization package.
MOBOpt
Multi-objective Bayesian optimization
Model-Based-Reinforcement-Learning-for-Online-Recommendation
A pytorch implementation of A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation.
open_spiel
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
ParetoMTL
Code for Neural Information Processing Systems (NeurIPS) 2019 paper: Pareto Multi-Task Learning
PGMORL
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
PRC-CMORL
This repo is the implementation of "Personalized Robotic Control via Constrained Multi-Objective Reinforcement Learning"
risk-and-uncertainty
Code associated with our paper "Estimating Risk and Uncertainty in Reinforcement Learning"
RL-PyT
This repository contains PyTorch implementations of reinforcement learning algorithms. Its purpose is to provide straightforward and easily understandable code for individuals to learn about basic reinforcement learning algorithms.
SA_DDPG
[NeurIPS 2020] State-adversarial DDPG for robust deep reinforcement learning
SD-GAR
This repository hosts the experimental code for NeurIPS 2020 paper "Sampling-Decomposable Generative Adversarial Recommender".
StateAdvDRL
[NeurIPS 2020, Spotlight]Code for "Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations"
Transformers-RL
An easy PyTorch implementation of "Stabilizing Transformers for Reinforcement Learning"
TransGAN
[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang