Papers with Code 아카이브

Q-Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

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

https://github.com/ashtawy/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/asjad99/Reinforcement-Learning

Deep Q-learning from Demonstrations

https://github.com/aspk/space_battle

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/atavakol/action-branching-agents

Deep Reinforcement Learning with Double Q-learning

https://github.com/atgambardella/pytorch-es

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/austinsilveria/Banana-Collection-DQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/autumnwu/aggressive-q-learning-with-ensembles

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

https://github.com/avillemin/Minecraft-AI

Playing Atari with Deep Reinforcement Learning

https://github.com/aviralkumar2907/BEAR

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

https://github.com/avivelor/UnityMachineLearningForProjectButterfly

Continuous control with deep reinforcement learning

https://github.com/axnedergaard/normalized-advantage-functions

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/b06b01073/Twin-Delayed-DDPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/b06b01073/continuous-control

Continuous control with deep reinforcement learning

https://github.com/bacdavid/HER

Continuous control with deep reinforcement learning

https://github.com/backgom2357/Recommender_system_via_deep_RL

Continuous control with deep reinforcement learning

https://github.com/baicenxiao/shaping-advice

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

https://github.com/baoqianwang/iros22_darl1n

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

https://github.com/baradist/multiagent-particle-envs

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

https://github.com/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/baturaysaglam/TCD3

Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods

https://github.com/baturaysaglam/dase

Continuous control with deep reinforcement learning

https://github.com/baturaysaglam/la3p

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

https://github.com/bay3s/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/behzaad/Deep_QLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/bidimensional/Tennis

Continuous control with deep reinforcement learning

https://github.com/biemann/Collaboration-and-Competition

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

https://github.com/biemann/Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/biorobotics/PRD_environments

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

https://github.com/bitterbloom/Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/bjotho/Zelda1AI

Playing Atari with Deep Reinforcement Learning

https://github.com/blakeMilner/DeepQLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/bmeyers/VirtualMicrogridSegmentation

Continuous control with deep reinforcement learning

https://github.com/bonniesjli/MADDPG_Tennis

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

https://github.com/bonniesjli/MADDPG_Tennis_UnityML

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

https://github.com/borea17/efficient_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/borhanreo/Obstacle-Avoid-Car

Playing Atari with Deep Reinforcement Learning

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