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Atari Games

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하위 태스크: 1
평가표: 65
보존된 평가 기록: 2,519

The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.

<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/PaulCharnay/Projet_AIF

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/PeterJochem/Deep_RL

Deep Reinforcement Learning with Double Q-learning

https://github.com/PeterJochem/Double_Deep_QLearning

Deep Reinforcement Learning with Double Q-learning

https://github.com/R-Stefano/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/R-Sweke/DeepQ-Decoding

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/RESQUELAB/RL-Teacher-UIAdaptation

Deep reinforcement learning from human preferences

https://github.com/RLAgent/state-marginal-matching

Unifying Count-Based Exploration and Intrinsic Motivation

https://github.com/RLeike/connect-four

Playing Atari with Deep Reinforcement Learning

https://github.com/Rabrg/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/RajGhugare19/dreamerv2

Mastering Atari with Discrete World Models

https://github.com/RamiSketcher/decision-transformer-ammi

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/RandyDeng/gym_connect4

Playing Atari with Deep Reinforcement Learning

https://github.com/Remtasya/DDPG-Actor-Critic-Reinforcement-Learning-Reacher-Environment

Prioritized Experience Replay

https://github.com/RevanMacQueen/LearningFromHumans

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

https://github.com/RexGLiu/rlpyt_crbp

Accelerated Methods for Deep Reinforcement Learning

https://github.com/RobotMobile/rl-paper-review

Playing Atari with Deep Reinforcement Learning

https://github.com/Roman-Kozachek/TradeBot

Deep Reinforcement Learning with Double Q-learning

https://github.com/SHRIVP/muzero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/SPark9625/Large-Scale-Study-of-Curiosity-Driven-Learning

Large-Scale Study of Curiosity-Driven Learning

https://github.com/SayhoKim/tetrisRL

Playing Atari with Deep Reinforcement Learning

https://github.com/SeungeonBaek/continuous-agents-test

Self-Imitation Learning

https://github.com/SeungeonBaek/discrete-agents-test

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ShangtongZhang/DistributedES

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Sheepsody/Batched-Impala-PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/ShibiHe/Q-Optimality-Tightening

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning_from_scratch

Playing Atari with Deep Reinforcement Learning

https://github.com/ShuhuaGao/gpFlappyBird

Evolving simple programs for playing Atari games

https://github.com/SimonRamstedt/ddpg

Prioritized Experience Replay

https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

Trust Region Policy Optimization

https://github.com/Suryavf/SelfDrivingCar

Prioritized Experience Replay

https://github.com/TheFebrin/DeepRL-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/TooTouch/3D_CoordConv_Segmentation

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/ToolManChang/DRLseminar_code_challenge

Soft Actor-Critic for Discrete Action Settings

https://github.com/V0LsTeR/DQN_heap

Prioritized Experience Replay

https://github.com/V0LsTeR/dopamine_prioritized_buffer

Prioritized Experience Replay

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