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Reinforcement Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/luthierman/quantum-research

Reinforcement Learning with Quantum Variational Circuits

https://github.com/luthierman/quantum-research-colab

Reinforcement Learning with Quantum Variational Circuits

https://github.com/luxxxlucy/recurrent-attention-model-revisited

Recurrent Models of Visual Attention

https://github.com/lvwerra/trl

Fine-Tuning Language Models from Human Preferences

https://github.com/lvyufeng/DQN-MindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/lweitkamp/option-critic-pytorch

The Option-Critic Architecture

https://github.com/lx10077/AveQLearning

Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning

https://github.com/lyebi/Test

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

https://github.com/lyp741/Pytorch-ERL

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/lyy1994/plm_as_kb

ParlAI: A Dialog Research Software Platform

https://github.com/m-serra/action-inference-for-video-prediction-benchmarking

Deep Visual Foresight for Planning Robot Motion

https://github.com/m5823779/DDPG

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

https://github.com/m5823779/MotionPlannerUsingDDPG

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

https://github.com/ma7555/Augz

GridMask Data Augmentation

https://github.com/machines-in-motion/grasping_sim

Leveraging Contact Forces for Learning to Grasp

https://github.com/madeleinesnyder/Subbat

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/madhur-tandon/RL-Project

Continuous control with deep reinforcement learning

https://github.com/madras-simulator/Multi-Agent-Particle-Environment

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

https://github.com/madvn/DDPG

Continuous control with deep reinforcement learning

https://github.com/magic-research/dataset_quantization

LIMA: Less Is More for Alignment

https://github.com/magister-informatica-uach/INFO267

An Introduction to Deep Reinforcement Learning

https://github.com/magnusja/ppo

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/mahaitongdae/safety_index_synthesis

Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

https://github.com/mahi97/XQMIX

The StarCraft Multi-Agent Challenge

https://github.com/mahshidhelali/RL-Assisted-Performance-Testing

An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning

https://github.com/majercakdavid/gym-virtual-quant-trading

Continuous control with deep reinforcement learning

https://github.com/makeyourownmaker/mixup

Regularizing Neural Networks by Penalizing Confident Output Distributions

https://github.com/malashinroman/least_action

Sparsely ensembled convolutional neural network classifiers via reinforcement learning

https://github.com/malerinc/fabricatio-rl

Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems

https://github.com/mangushev/inventory_management

Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

https://github.com/manhdung20112000/RL-Image-Classification

Natural Environment Benchmarks for Reinforcement Learning

https://github.com/manshri/tesum

A Deep Reinforced Model for Abstractive Summarization

https://github.com/mantoone/ml-agents

Unity: A General Platform for Intelligent Agents

https://github.com/manvibharat/Stock-price-pridiction-using-Deep-reienforcement-learning

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/maraghuram/I-DQN

Towards Better Interpretability in Deep Q-Networks

https://github.com/marcovirgolin/UncertaintyEstimationInDeepNets

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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