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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/brain-research/LeaveNoTrace

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

https://github.com/brain-research/mirage-rl

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

https://github.com/brando130/CSharp_NN

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/brandontrabucco/efficient-hrl

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/brian-moser/dartsrenet

DARTS: Differentiable Architecture Search

https://github.com/bruzwen/ddxplus

Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification

https://github.com/bryonkucharski/Learning-to-Drive-with-Reinforcement-Learning-and-Variational-Autoencoders

Learning to Drive in a Day

https://github.com/bryonkucharski/learning-to-drive-in-a-day-reproduction

Learning to Drive in a Day

https://github.com/bucky1995/Portfolio-RL

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/bukysun/gail-traj-eb

Generative Adversarial Imitation Learning

https://github.com/cair/PyTsetlinMachineCUDA

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/pyTsetlinMachine

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/pyTsetlinMachineMT

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/pyTsetlinMachineParallel

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/caiyingchun/MolDQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/calclavia/NOS

Neural Optimizer Search with Reinforcement Learning

https://github.com/camall3n/atari-MAC

Mean Actor Critic

https://github.com/cannylab/casual_overhypotheses

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/carlospurves/psxle

The PlayStation Reinforcement Learning Environment (PSXLE)

https://github.com/carpedm20/ENAS-pytorch

Neural Architecture Search with Reinforcement Learning

https://github.com/carperai/trlx

Fine-Tuning Language Models from Human Preferences

https://github.com/caslab-vt/SARNet

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/cataluna84/WorldModels_v1.0

https://github.com/cathywu/rllab-multiagent

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cav-research-lab/predictive-model-delay-correction

Reinforcement Learning with Random Delays

https://github.com/caymansimpson/reuniclusVGC

Game-Theoretic Multiagent Reinforcement Learning

https://github.com/cbellinger27/adaptive_optics_gym

https://github.com/cbellinger27/semi-MarkovRLForMatrials

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

https://github.com/cbschaff/nlimb

Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning

https://github.com/cda-tum/mqt-predictor

Compiler Optimization for Quantum Computing Using Reinforcement Learning

https://github.com/cdesilv1/sc2_ai_cdes

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/cejuicreamice/R_easy_RL_fix

Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission Execution

https://github.com/cesch97/NeuroEvolution

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/chaitjo/graph-convnet-tsp

On Learning Paradigms for the Travelling Salesman Problem

https://github.com/chaitjo/learning-tsp

Neural Combinatorial Optimization with Reinforcement Learning

https://github.com/chamorajg/pl-dreamer

Dream to Control: Learning Behaviors by Latent Imagination

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.