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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/trackmania-rl/tmrl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/traffic-alpha/tsc-harla

iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement

https://github.com/transedward/pytorch-hdqn

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/trisongz/question-answering-lightning

Machine Comprehension by Text-to-Text Neural Question Generation

https://github.com/tristandeleu/pytorch-maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/tristandeleu/pytorch-maml-rl

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/tsinghua-rll/gym-gridworld

https://github.com/ttajmajer/morl-dv

Modular Multi-Objective Deep Reinforcement Learning with Decision Values

https://github.com/tuantran23012000/Recommendation-system

Deep Reinforcement Learning for List-wise Recommendations

https://github.com/tuomaso/SC2LE-implementation

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/turningpoint-ai/visualthinker-r1-zero

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

https://github.com/tushar-agarwal2909/Bayes_by_Backprop-TF

Weight Uncertainty in Neural Networks

https://github.com/typoverflow/OfflineRL-Lib

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

https://github.com/uber-archive/plato-research-dialogue-system

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

https://github.com/uber-research/ape-x

Distributed Prioritized Experience Replay

https://github.com/uber-research/ga-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/uber/fiber

Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

https://github.com/ucl-dark/pax

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/ucsc-vlaa/vlaa-thinking

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

https://github.com/ugo-nama-kun/DQN-chainer

Playing Atari with Deep Reinforcement Learning

https://github.com/uidmice/placement-rl

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

https://github.com/ukachyuthan/Rewards_in_RL

Reward Engineering for Object Pick and Place Training

https://github.com/ulissigroup/uncertainty_benchmarking

Accurate Uncertainties for Deep Learning Using Calibrated Regression

https://github.com/ulstu/ml

Deep Attention Recurrent Q-Network

https://github.com/ulstu/robotics_ml

Deep Attention Recurrent Q-Network

https://github.com/umd-huang-lab/reinforcement-learning-via-spectral-methods

Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning

https://github.com/umersheikh846/WQMIX

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/uncharted-technologies/risk-and-uncertainty

Estimating Risk and Uncertainty in Deep Reinforcement Learning

https://github.com/unreliableXu/TensorFlow_RLRE

Reinforcement Learning for Relation Classification from Noisy Data

https://github.com/unstablecursor/robotino_rl

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

https://github.com/uoe-agents/ksl

Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning

https://github.com/uoe-agents/revisiting-maddpg

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

https://github.com/uoe-agents/sami

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/uos-plccn/gym-pbn

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/upb-lea/gem-control

Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

https://github.com/uribyul/py_ai_clinician

Deep Reinforcement Learning for Sepsis Treatment

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