Papers with Code 아카이브

Reinforcement Learning

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

https://github.com/poi233/pcgrl_rts

PCGRL: Procedural Content Generation via Reinforcement Learning

https://github.com/pointerformer/pointerformer

Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem

https://github.com/pokaxpoka/RAD_state_aug

Benchmarking Model-Based Reinforcement Learning

https://github.com/pokaxpoka/rad_openaigym

Benchmarking Model-Based Reinforcement Learning

https://github.com/ponyfat/graph-conjectures-counterexaples

Constructions in combinatorics via neural networks

https://github.com/poppinace/libranet

Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

https://github.com/porouspaper/games-test

Unity: A General Platform for Intelligent Agents

https://github.com/porouspaper/svo-games

Unity: A General Platform for Intelligent Agents

https://github.com/potatoehacker/ArticleDeepNeuroEvolution

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

https://github.com/potsawee/maml

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

https://github.com/pr-shukla/maddpg-keras

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

https://github.com/pra-dan/Worth-Reading-ML-Papers

Model compression via distillation and quantization

https://github.com/prajjwal1/autodial

ParlAI: A Dialog Research Software Platform

https://github.com/prajjwal1/fluence

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

https://github.com/prajwalgatti/DRL-Collaboration-and-Competition

Continuous control with deep reinforcement learning

https://github.com/prajwalgatti/DRL-Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/prajwalgatti/DRL-Navigation

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/pranz24/pytorch-soft-actor-critic

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

https://github.com/prasad-kumkar/openai-taxi-v3

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

https://github.com/prasanns/rlhf-length-biases

RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment

https://github.com/prasoongoyal/rl-learn

Using Natural Language for Reward Shaping in Reinforcement Learning

https://github.com/preetam1997/Self-Driven-Car

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/premiertaco/premier-taco

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

https://github.com/priorelli/dynamic-planning

Deep hybrid models: infer and plan in a dynamic world

https://github.com/priyavrat-misra/xrays-and-gradcam

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/project-basileus/multitype-sequence-generation-by-tlstm-gan

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/pschaldenbrand/ContentMaskedLoss

Learning to Paint With Model-based Deep Reinforcement Learning

https://github.com/psh150204/MAML

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

https://github.com/puppetect/TradingBot-tensorflow

Deep Reinforcement Learning with Double Q-learning

https://github.com/pwc-1/Paper-8/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pwc-1/Paper-9/tree/main/3/translating-math-formula-images

Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

https://github.com/pwc-1/Paper-9/tree/main/4/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/pwc-1/Paper-9/tree/main/4/rand_conv-master

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

https://github.com/pwc-1/Paper-9/tree/main/4/translating-math-formula-images

Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

https://github.com/pwc-1/Paper-9/tree/main/7/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/pwc-1/Paper-9/tree/main/7/Simba

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

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