https://github.com/qihongl/demo-random-search-RL
Simple random search provides a competitive approach to reinforcement learning
Papers with Code 아카이브
Reinforcement Learning (RL)
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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/qihongl/dlstm-demo
https://github.com/qingzwang/DiverseImageCaptioning
Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process
https://github.com/qinwang/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/qmaai/open_spiel
https://github.com/qq303067814/DQLearning-Toolbox
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/qq456cvb/multi-stage-detection
https://github.com/quantaji/AMD-SSD
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
https://github.com/quantum-fusion/AI-deeplearning
https://github.com/quantumiracle/Popular-RL-Algorithms
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/quantumiracle/mars
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
https://github.com/quanvuong/handful-of-trials-pytorch
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/qyuan7/RNN_RL_molecule
Molecular De Novo Design through Deep Reinforcement Learning
https://github.com/qyuan7/RNN_TL_molecule
Molecular De Novo Design through Deep Reinforcement Learning
https://github.com/raccoon831012/StartCraft2-RL
https://github.com/racheljose21/chatbot
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/ragen-ai/vagen
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
https://github.com/rahul263-stack/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/rahulpandeycs/openspiel-games
https://github.com/rainandwind1/MADDPG-reconstruct
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rainandwind1/MERL
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rainandwind1/Maddpg_multiagent
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/raja17021998/Augmented-Random-Search-Implementation
Simple random search provides a competitive approach to reinforcement learning
https://github.com/rajahaseeb147/PCD_Registration_ReAgent
ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning
https://github.com/rallen10/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/ram81/il_rl_baselines
PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav
https://github.com/ramanans1/dm_control
https://github.com/ran-weii/cleanil
https://github.com/ran-weii/objective_mismatch_papers
https://github.com/rangwani-harsh/PC_Grad_Pytorch
https://github.com/raoshashank/Tennis-with-MADDPG
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rasmusbergpalm/evostrat
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
https://github.com/rasmusbergpalm/hebbian-evolution
https://github.com/ray-project/ray/tree/master/rllib
https://github.com/rayanramoul/Active-Object-Localization-Deep-Reinforcement-Learning
https://github.com/raysr/Active-Object-Localization-Deep-Reinforcement-Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.