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/puppetect/TradingBot-tensorflow

Deep Reinforcement Learning with Double Q-learning

https://github.com/pytorch/rl/tree/main/examples/cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/iql

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pytorch/rl/tree/main/examples/sac

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

https://github.com/pytorch/rl/tree/main/examples/td3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/qi-pang/mdpfuzz

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

https://github.com/qiankun214/DQN-FlappyBird-python3

Playing Atari with Deep Reinforcement Learning

https://github.com/quantumiracle/Popular-RL-Algorithms

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

https://github.com/quantumiracle/consistency_model_for_reinforcement_learning

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

https://github.com/quasimetric-learning/torch-quasimetric

On the Learning and Learnability of Quasimetrics

https://github.com/rainandwind1/MADDPG-reconstruct

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

https://github.com/rainandwind1/MERL

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

https://github.com/rainandwind1/Maddpg_multiagent

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

https://github.com/rallen10/multiagent-particle-envs

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

https://github.com/raoshashank/Tennis-with-MADDPG

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

https://github.com/rasmusbergpalm/evostrat

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/ray-project/ray/tree/master/rllib

Playing Atari with Deep Reinforcement Learning

https://github.com/rebekkaorth/robotics_pick_n_place_masterproject

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

https://github.com/reiniscimurs/TD3_Separate_Action

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/rikluost/RL_DQN_Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/rishavb123/MineRL

Playing Atari with Deep Reinforcement Learning

https://github.com/rk1998/robot-sac

Continuous control with deep reinforcement learning

https://github.com/robintyh1/icml2021-pengqlambda

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/roythuly/obac

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

https://github.com/rshnn/battleship

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/runamu/compositional-conservatism

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/rybread1/DeepRlTrex

Deep Reinforcement Learning with Double Q-learning

https://github.com/rybread1/deep-rl-trex

Deep Reinforcement Learning with Double Q-learning

https://github.com/s-sd/task-amenability

Continuous control with deep reinforcement learning

https://github.com/sagpant/ViZDoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/saha0073/Deep-Reinforcement-Learning-to-play-Cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/sail-sg/offbench

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/samiranrl/ODSC_2019_RL

Continuous control with deep reinforcement learning

https://github.com/samuelmat19/DDPG-tf2

Continuous control with deep reinforcement learning

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

Continuous control with deep reinforcement learning

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