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Q-Learning

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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/darkrush/meta_learn

Continuous control with deep reinforcement learning

https://github.com/darshil333/CSE574

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

https://github.com/dasgringuen/assetto_corsa_gym

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

https://github.com/david-abel/continuous_state_sa

Learning State Abstractions for Transfer in Continuous Control

https://github.com/daviddcho/supermario

Playing Atari with Deep Reinforcement Learning

https://github.com/dchetelat/acer

Continuous control with deep reinforcement learning

https://github.com/debajit15kgp/multiagent-envs

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

https://github.com/deepmind/rlax

Deep Reinforcement Learning with Double Q-learning

https://github.com/denizmguen/IANNWTF2019-Project

Continuous control with deep reinforcement learning

https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum

Continuous control with deep reinforcement learning

https://github.com/div99/xql

Extreme Q-Learning: MaxEnt RL without Entropy

https://github.com/dmeoli/neuro-sat

Can $Q$-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

https://github.com/dmksjfl/smr

Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse

https://github.com/donamin/llc

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

https://github.com/dongchen06/macacc

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/dpoulopoulos/drl_collaborate_compete

Continuous control with deep reinforcement learning

https://github.com/dpoulopoulos/drl_continuous_control

Continuous control with deep reinforcement learning

https://github.com/drforester/Q-learning-Intersection-Crossing

Playing Atari with Deep Reinforcement Learning

https://github.com/dsgiitr/rl_2048

Playing Atari with Deep Reinforcement Learning

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

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

https://github.com/dxyang/DQN_pytorch

Deep Reinforcement Learning with Double Q-learning

https://github.com/dylancashman/remap_nas

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/dyth/doublegum

Continuous control with deep reinforcement learning

https://github.com/eac-replication/eac-replication

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

https://github.com/eddynelson/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/edmundmills/basalt-competition

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

https://github.com/elenathFGS/BlockQNN_Keras

Practical Block-wise Neural Network Architecture Generation

https://github.com/emiliojorge/Inventing-a-Grounded-Language

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

https://github.com/enajx/ES

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/epignatelli/human-level-control-through-deep-reinforcement-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/esmeralday/MARL

Playing Atari with Deep Reinforcement Learning

https://github.com/eublefar/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/evaboost/evaboost

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/fabiopardo/qmap

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

https://github.com/facebookresearch/Horizon

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/ReAgent

Deep Reinforcement Learning with Double Q-learning

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