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/mkocabas/CoordConv-pytorch

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/ml-jku/l2m

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/mmasana/FACIL

Overcoming catastrophic forgetting in neural networks

https://github.com/mng827/curve-gcn-cardiac-mr

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/moduIo/Deep-Q-network

Deep Reinforcement Learning with Double Q-learning

https://github.com/mohit8935/Deep-Q-Learning-Paper

Deep Reinforcement Learning with Double Q-learning

https://github.com/mohith-sakthivel/rainbow_dqn

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/molomono/CartPole_Optimized_DDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/monte-ai/expert-augmented-acktr

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

https://github.com/mrahtz/learning-from-human-preferences

Deep Exploration via Bootstrapped DQN

https://github.com/mu-y/lll-tts

Overcoming catastrophic forgetting in neural networks

https://github.com/muupan/async-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/nancyhwr/DQN_Ram

Learning from the memory of Atari 2600

https://github.com/nandomp/AICollaboratory

The Arcade Learning Environment: An Evaluation Platform for General Agents

https://github.com/nathanin/pad

Playing Atari with Deep Reinforcement Learning

https://github.com/natsumeS/analysis

Playing Atari with Deep Reinforcement Learning

https://github.com/nbopardi/smb

Deep Reinforcement Learning with Double Q-learning

https://github.com/near32/regym

Playing Atari with Deep Reinforcement Learning

https://github.com/neilsgp/RL-Algorithms

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/neka-nat/distributed_rl

Distributed Prioritized Experience Replay

https://github.com/neuro-soup/es-torch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/nhynes/abc

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

https://github.com/nikaashpuri/sarfa-saliency

Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

https://github.com/nikhilbarhate99/min-decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/niklasschmitz/DeepQLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/ninja18/AtariDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/nishantgurunath/HRL

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/nitthilan/pommerman

Trust Region Policy Optimization

https://github.com/nnaisense/pgpelib

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/nolanwinsman/Team-Doom

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

https://github.com/nolisten/erl

Count-Based Exploration with Neural Density Models

https://github.com/nvlabs/gbrl_sb3

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/nvshrao/Elastic-Weight-Consolidation

Overcoming catastrophic forgetting in neural networks

https://github.com/odokumaci/rainbow-unity-obstacle-tower-challenge

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/ofekluis/sonic_project_ss19

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/olonok69/Udacity_Banana_Unity

Prioritized Experience Replay

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