wulihan20212021's Stars
GaiZhenbiao/ChuanhuChatGPT
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
LantaoYu/MARL-Papers
Paper list of multi-agent reinforcement learning (MARL)
facebookresearch/rebel
An algorithm that generalizes the paradigm of self-play reinforcement learning and search to imperfect-information games.
chenhongge/StateAdvDRL
[NeurIPS 2020, Spotlight] Code for "Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations"
santhisenan/SDN_DDoS_Simulation
An attempt to detect and prevent DDoS attacks using reinforcement learning. The simulation was done using Mininet.
matlab-deep-learning/rl-agent-based-traffic-control
Develop agent-based traffic management system by model-free reinforcement learning
p-casgrain/Nash-DQN
Deep Reinforcement Learning for Nash Equilibria
asokraju/Adv-MARL
Adversarial attacks in consensus-based multi-agent reinforcement learning
DeepakKarishetti/Reinforcement_learning-PID-auto-tuning
Auto tuning of PID parameters of a quad-rotor using Q-learning
ddfan/swarm_evolve
Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms
wangbx66/differentially-private-q-learning
apizbakar/Soft-Actor-Critic-Reinforcement-Learning-Mobile-Robot-Navigation
This example uses Soft Actor Critic(SAC) based reinforcement learning to develop the mobile robot navigation. For a brief summary of the SAC algorithm, see Soft Actor Critic(SAC) Agents. This example scenario trains a mobile robot to navigate from location A to location B to avoid obstacles given range sensor readings that detect obstacles in the map. The objective of the reinforcement learning algorithm is to learn what controls (linear and angular velocity) for navigation from an initial to goal position and during the travel also can avoid colliding into obstacles. This example uses an occupancy map of a known environment to generate range sensor readings, detect obstacles, and check collisions the robot may make. The range sensor readings are the observations for the SAC agent, and the linear and angular velocity controls are the action.
semanticweights/tarok
:spades: Slovenian Tarok card game environment for the OpenSpiel framework.
mnecipkurt/tsg19
DSS-lab/DRLCyberAssessment_DQNCode
abhisikdar/Quickest-Detection-FDI-Remote-Estimation
Code for our paper titled "Quickest detection of false data injection in remote state estimation" published at IEEE ISIT 2021.
xahiru/NetworkSecRLwithPareto
Network Security Attack and Defence Strategy selection using Reinforcement Learning and Pareto efficiency
peweetheman/Reinforcement_Learning_In_Two_Player_Simultaneous_Action_Games