Papers with Code 아카이브

Reinforcement Learning (RL)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

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/jakegrigsby/deep_control/blob/master/deep_control/sac.py

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

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/td3.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/jakegrigsby/supersonic

Exploration by Random Network Distillation

https://github.com/jakub-zarzycki2022/gym-pbn-stac

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/jangirrishabh/Overcoming-exploration-from-demos

Hindsight Experience Replay

https://github.com/jangirrishabh/gym

https://github.com/janhuenermann/neurojs

Increasing the Action Gap: New Operators for Reinforcement Learning

https://github.com/jankrepl/mildlyoverfitted

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

https://github.com/jansenkeith501/CS295-MADDPG

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

https://github.com/jasonisgod/Big2

Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information

https://github.com/jasonrobwebster/alphazero-clone

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

https://github.com/jaylee0301/dhrl_official

DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning

https://github.com/jbinas/gym-mnist

Variational Intrinsic Control

https://github.com/jbrea/BayesianOptimization.jl

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/jchenghu/sacreeos

Self-critical Sequence Training for Image Captioning

https://github.com/jcoreyes/evolvingrl

Evolving Reinforcement Learning Algorithms

https://github.com/jcoreyes/ex2

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

https://github.com/jcwleo/curiosity-driven-exploration-pytorch

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/jcwleo/random-network-distillation-pytorch

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/jeanharb/option_critic

The Option-Critic Architecture

https://github.com/jeapostrophe/monaco

A0C: Alpha Zero in Continuous Action Space

https://github.com/jeffchy/Artificial-Idiot

Playing FPS Games with Deep Reinforcement Learning

https://github.com/jeffery1236/Atari_DoubleDeepQNetwork

Deep Reinforcement Learning with Double Q-learning

https://github.com/jemaw/gym-safety

Constrained Policy Optimization

https://github.com/jeonghun3572/CoCoS

Training Language Models to Self-Correct via Reinforcement Learning

https://github.com/jerrylsu/TC-Bot

A User Simulator for Task-Completion Dialogues

https://github.com/jesbu1/carl

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

https://github.com/jezzarax/drlnd_p1_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/jfpettit/jfpettit.github.io

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning

https://github.com/jhejna/dwsl

Distance Weighted Supervised Learning for Offline Interaction Data

https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-Attention

Self-critical Sequence Training for Image Captioning

https://github.com/jiahuei/COMIC-Towards-A-Compact-Image-Captioning-Model-with-Attention

Self-critical Sequence Training for Image Captioning

https://github.com/jiahuei/Self-Critical-SCST-TensorFlow

Self-critical Sequence Training for Image Captioning

https://github.com/jiangyingjunn/i-darts

DARTS: Differentiable Architecture Search

https://github.com/jiayu-ch15/MPE-for-curriculum-learning

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

https://github.com/jiechuanjiang/pytorch_dgn

Graph Convolutional Reinforcement Learning

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