Papers with Code 아카이브

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/eladsar/rbi

Distributed Prioritized Experience Replay

https://github.com/emiled16/Beyond_prioritized_experience_replay

Prioritized Experience Replay

https://github.com/enajx/ES

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/enpasos/muzero

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/eparisotto/ActorMimic

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

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

Playing Atari with Deep Reinforcement Learning

https://github.com/ercumentilhan/MinAtar

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

https://github.com/eric-yim/fin_map

A Distributional Perspective on Reinforcement Learning

https://github.com/ermongroup/higher_order_invariance

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/esmeralday/MARL

Playing Atari with Deep Reinforcement Learning

https://github.com/ethanmclark1/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/ethz-asl/background_foreground_segmentation

Overcoming catastrophic forgetting in neural networks

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

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/facebookresearch/ReAgent

Deep Reinforcement Learning with Double Q-learning

https://github.com/facebookresearch/gala

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/facebookresearch/minihack

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

https://github.com/facebookresearch/rl/blob/main/examples/dqn/dqn.py

Playing Atari with Deep Reinforcement Learning

https://github.com/facebookresearch/salina

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/facebookresearch/torchbeast

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/fapont/hackaton-hiparis-2021

Momentum-based Accelerated Q-learning

https://github.com/farama-foundation/vizdoom

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

https://github.com/felixriese/CNN-SoilTextureClassification

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/fengsterooni/dql

Deep Reinforcement Learning with Double Q-learning

https://github.com/fiberleif/evolution-strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/filippogiruzzi/deep_q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/fionisarnen/Lifelong-Learning-churn-prediction-torch

Overcoming catastrophic forgetting in neural networks

https://github.com/floringogianu/atari-agents

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

https://github.com/fmorenovr/Attention_Mechanism

Performing Deep Recurrent Double Q-Learning for Atari Games

https://github.com/foersterrobert/MuZero

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

https://github.com/fomorians/forward-models

Recurrent Environment Simulators

https://github.com/forevergj/multithreading_ppo_RND

Exploration by Random Network Distillation

https://github.com/fuyuan-li/tensorflow-RIMs

Recurrent Independent Mechanisms

https://github.com/g-u-n/pycil

Overcoming catastrophic forgetting in neural networks

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