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/opendilab/DI-engine

Deep Reinforcement Learning with Double Q-learning

https://github.com/opendilab/DI-engine/blob/main/ding/entry/serial_entry_sqil.py

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

https://github.com/opendilab/DI-engine/blob/main/ding/policy/collaq.py

Multi-Agent Collaboration via Reward Attribution Decomposition

https://github.com/opendilab/DI-engine/blob/main/ding/policy/sac.py

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

https://github.com/opendilab/DI-engine/blob/main/ding/policy/sql.py

Reinforcement Learning with Deep Energy-Based Policies

https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/opent03/neural_cars

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

https://github.com/optimization-toolbox/DNE4py

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

https://github.com/optimization-toolbox/deep-neuroevolution-mpi4py

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

https://github.com/orrivlin/implicit-q-learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/oxwhirl/comix

FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/oxwhirl/facmac

FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/p-casgrain/Nash-DQN

Deep Q-Learning for Nash Equilibria: Nash-DQN

https://github.com/paintception/Deep-Quality-Value-DQV-Learning-

Deep Quality-Value (DQV) Learning

https://github.com/paintception/Deep-Quality-Value-Family

Playing Atari with Deep Reinforcement Learning

https://github.com/parilo/rl-server

Playing Atari with Deep Reinforcement Learning

https://github.com/pathway/alphaxos

Deep Reinforcement Learning with Double Q-learning

https://github.com/patniemeyer/ga-autoencoder

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/patrickhart/jaxdl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/pavitrakumar78/Playing-custom-games-using-Deep-Learning

Playing Atari with Deep 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/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/pkasala/ContinuesControl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/polixir/NeoRL

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

https://github.com/pondbooks/CDQL_with_Sim

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

https://github.com/potatoehacker/ArticleDeepNeuroEvolution

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

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

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

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/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/proroklab/popgym

Playing Atari with Deep Reinforcement Learning

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