Papers with Code 아카이브

Video Games

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/gstoica27/cpg_ppo

Proximal Policy Optimization Algorithms

https://github.com/guillaumeboniface/bananaland

Deep Reinforcement Learning with Double Q-learning

https://github.com/gungui98/deeprl-a3c-ai2thor

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/guofeng201507/ISA-IPA-2020-05-05-IS1PT-GRP-High5-StockTrading

Proximal Policy Optimization Algorithms

https://github.com/gwthomas/gtml

Proximal Policy Optimization Algorithms

https://github.com/gznyyb/deep_reinforcement_learning_Pong

Deep Reinforcement Learning with Double Q-learning

https://github.com/haje01/distper

Distributed Prioritized Experience Replay

https://github.com/haje01/impala

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

https://github.com/hal-314/fastai-batch-size-finder

An Empirical Model of Large-Batch Training

https://github.com/hamishs/JAX-RL

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/han-won/PlayingAtariWithMindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/harruff/Senior_Project_Repository

Trust Region Policy Optimization

https://github.com/harvitronix/reinforcement-learning-car

Playing Atari with Deep Reinforcement Learning

https://github.com/hcch0912/gym_wrapper_dm_soccer

Arena: a toolkit for Multi-Agent Reinforcement Learning

https://github.com/hdparks/AsteroidsDeepReinforcement

Proximal Policy Optimization Algorithms

https://github.com/hegde95/Agents_that_Listen

Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems

https://github.com/hegde95/ViZDoom_with_Sound

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

https://github.com/heiner/scalable_agent

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

https://github.com/hemilpanchiwala/Dueling-Network-Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Dueling_Network_Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hhhusiyi-monash/UPDeT

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/hill-a/stable-baselines

Playing Atari with Deep Reinforcement Learning

https://github.com/hmhuy0/SIM-RL

Proximal Policy Optimization Algorithms

https://github.com/htdt/rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/huggingface/transformers

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/huihanl/rl_unplugged

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

https://github.com/hulanwin/A3C-DRL

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/hw9603/DQfD-PyTorch

Noisy Networks for Exploration

https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient

Prioritized Experience Replay

https://github.com/iDataist/Navigation-with-Deep-Q-Network

Prioritized Experience Replay

https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement

Prioritized Experience Replay

https://github.com/iankuoli/DFNet_TF2

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/ianlimle/ItsMeMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/icmlanon58443043/vizdoom

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

https://github.com/icmlanon58443043/vizdoomicmlanon

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

https://github.com/idsia/modern-srwm

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

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