Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/chinganc/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/chiragsamal/Importance-Sampling-in-Reinforcement-Learning

Importance Resampling for Off-policy Prediction

https://github.com/chorowski-lab/CPC_audio

Representation Learning with Contrastive Predictive Coding

https://github.com/chorowski-lab/hcpc

Representation Learning with Contrastive Predictive Coding

https://github.com/christiancosgrove/cs767final

ParlAI: A Dialog Research Software Platform

https://github.com/chung771026/Implement-seqGAN-with-Keras

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/cindycia/Atari-SAC-Discrete

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

https://github.com/cipher982/ppo-exploration

Proximal Policy Optimization Algorithms

https://github.com/cityflow-project/cityflower

CoLight: Learning Network-level Cooperation for Traffic Signal Control

https://github.com/cjlovering/Towards-Interpretable-Reinforcement-Learning-Using-Attention-Augmented-Agents-Replication

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

https://github.com/cjohnchen/changebale

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/cjohnchen/mirror

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/cjohnchen/sai

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/cjy1992/gym-carla

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

https://github.com/ckinateder/pytsetlinmachineparallel

Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/ckjzsa/wwtp_opt_rl

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

https://github.com/clarky104/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/claudeHifly/BipedalWalker-v3

Continuous control with deep reinforcement learning

https://github.com/clear-nus/MuMMI

Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning

https://github.com/clementbernardd/Count-Based-Exploration

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

https://github.com/clic-lab/ciff

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

https://github.com/clin366/MFN

Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning

https://github.com/clinplayer/3DModelingRL

Modeling 3D Shapes by Reinforcement Learning

https://github.com/clrrrr/promp_plus

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

https://github.com/clvoloshin/constrained_batch_policy_learning

Batch Policy Learning under Constraints

https://github.com/clvrai/furniture

IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

https://github.com/clvrai/leaps

Learning to Synthesize Programs as Interpretable and Generalizable Policies

https://github.com/clvrai/skild

Demonstration-Guided Reinforcement Learning with Learned Skills

https://github.com/clwainwright/proximal_policy_optimization

Proximal Policy Optimization Algorithms

https://github.com/cmu-safari/hermes

Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning

https://github.com/cocolico14/N-step-Dueling-DDQN-PER-Pacman

Deep Reinforcement Learning with Double Q-learning

https://github.com/code-implementation1/Code1/tree/main/DecoMR

DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning

https://github.com/code-implementation1/Code5/tree/main/MAML

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

https://github.com/code-implementation1/Code6/tree/main/pnasnet

Progressive Neural Architecture Search

https://github.com/code-implementation1/Code7/tree/main/RNA

Learning to Design RNA

https://github.com/code-implementation1/Code9/tree/main/cflownets

CFlowNets: Continuous Control with Generative Flow Networks

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