Papers with Code 아카이브

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/pengyuan-zhou/Multi-agent-RL-traffic-light-control

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/penn-pal-lab/peg

Skew-Fit: State-Covering Self-Supervised Reinforcement Learning

https://github.com/pereirarodrigo/project_spike

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/pereirarodrigo/spike

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/perrin-isir/afu

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pervasive-ai-lab/crlmaze

Continual Reinforcement Learning in 3D Non-stationary Environments

https://github.com/pervasive-ai-lab/nle-language-wrapper

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/peterhj/arm-pytorch

Regret Minimization for Partially Observable Deep Reinforcement Learning

https://github.com/petsol/ContinuousControl_UnityAgent_DDPG_Udacity

Continuous control with deep reinforcement learning

https://github.com/petsol/MultiAgentCooperation_UnityAgent_MADDPG_Udacity

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

https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code

Deep Reinforcement Learning with Double Q-learning

https://github.com/philtabor/Multi-Agent-Deep-Deterministic-Policy-Gradients

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

https://github.com/phixion/phixion

Towards Learning to Play Piano with Dexterous Hands and Touch

https://github.com/piEsposito/blitz-bayesian-deep-learning

Weight Uncertainty in Neural Networks

https://github.com/pigfarm/LQAC_code

Exact Asymptotics for Linear Quadratic Adaptive Control

https://github.com/pihey1995/DistributionalRL

A Distributional Perspective on Reinforcement Learning

https://github.com/pikaju/pikaju

Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

https://github.com/pipilurj/BONAS

DARTS: Differentiable Architecture Search

https://github.com/pjhool/TF-A2RL-Test

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/pkasala/ContinuesControl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/pku-rl/dgn

Graph Convolutional Reinforcement Learning

https://github.com/plaffa/discrete-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/pliang279/MFN

Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning

https://github.com/plkmo/AlphaZero_Connect4

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

https://github.com/pocokhc/agent57

Agent57: Outperforming the Atari Human Benchmark

https://github.com/poi233/pcgrl_rts

PCGRL: Procedural Content Generation via Reinforcement Learning

https://github.com/pokaxpoka/RAD_state_aug

Benchmarking Model-Based Reinforcement Learning

https://github.com/pokaxpoka/b_pref

PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

https://github.com/pokaxpoka/rad_openaigym

Benchmarking Model-Based Reinforcement Learning

https://github.com/polixir/OfflineRL

MOPO: Model-based Offline Policy Optimization

https://github.com/pondbooks/CDQL_with_Sim

Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems

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/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

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