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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/stavrosgreece/MultiAgentLearning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/stijani/elastic-weight-consolidation-tf2

Overcoming catastrophic forgetting in neural networks

https://github.com/subhadip-maiti/tinydqn

Playing Atari with Deep Reinforcement Learning

https://github.com/sunfex/weighted-sac

Prioritized Experience Replay

https://github.com/sunjeet95/Deep-Q-Network-using-Tensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/susanwe/ope_worldmodels

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/suyoung-lee/Episodic-Backward-Update

Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening

https://github.com/sygi/deep_q_rl

Playing Atari with Deep Reinforcement Learning

https://github.com/szrlee/GPT-HyperAgent

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

https://github.com/szrlee/ensemble_plus_plus

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

https://github.com/takuseno/d3rlpy

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/tcmxx/CNTKUnityTools

Playing Atari with Deep Reinforcement Learning

https://github.com/tensorflow/models

Deep Exploration via Bootstrapped DQN

https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits

Deep Exploration via Bootstrapped DQN

https://github.com/tensorlayer/RLzoo

Trust Region Policy Optimization

https://github.com/tensorpack/tensorpack/tree/master/examples/A3C-Gym

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/tensorpack/tensorpack/tree/master/examples/DeepQNetwork

Playing Atari with Deep Reinforcement Learning

https://github.com/theSparta/neural-symbolic-machines

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

https://github.com/thomas-schillaci/SimPLe

Model-Based Reinforcement Learning for Atari

https://github.com/thomashirtz/noisy-networks

Noisy Networks for Exploration

https://github.com/threewisemonkeys-as/torched_impala

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

https://github.com/thu-ml/tianshou

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/tkcoding/DeepRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tkcoding/Stock_DRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/tlikhomanenko/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/tlohr/nfsu2-ai

Playing Atari with Deep Reinforcement Learning

https://github.com/toni-sm/skrl

Playing Atari with Deep Reinforcement Learning

https://github.com/tophatraptor/si-transfer

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

https://github.com/toshikwa/soft-actor-critic.pytorch

Prioritized Experience Replay

https://github.com/toybox-rs/Toybox

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

https://github.com/toybox-rs/openai-baselines-envs

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

https://github.com/tphanson/xupr-drl

Prioritized Experience Replay

https://github.com/traai/async-deep-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/transedward/pytorch-hdqn

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/typoverflow/OfflineRL-Lib

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/uam-biometrics/Transformer-Networks

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

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