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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/LukasGardberg/cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/LukeBolly/dreamerv2

Mastering Atari with Discrete World Models

https://github.com/Lyusungwon/apex_dqn_pytorch

Distributed Prioritized Experience Replay

https://github.com/MEOWMEOW114/nd893-p1-navigation-banana

Deep Reinforcement Learning with Double Q-learning

https://github.com/MOVzeroOne/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/MateuszJanda/netris-ai-robot

Playing Atari with Deep Reinforcement Learning

https://github.com/MathPhysSim/PER-NAF

Prioritized Experience Replay

https://github.com/MatheusMRFM/A3C-LSTM-with-Tensorflow

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/MaxSobolMark/HardRLWithYoutube

Playing hard exploration games by watching YouTube

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Reinforcement_Learning_with_Double_Q_learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Playing_Atari_with_Deep_Reinforcement_Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Playing Atari with Deep Reinforcement Learning

https://github.com/Miatto-research-group/muzero

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

https://github.com/MilkSilk/masters_thesis

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/MindCode-4/code-11/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-13/tree/main/TRSTMI

Trust Region Policy Optimization

https://github.com/MindCode-4/code-3/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/MindCode-4/code-6/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-8/tree/main/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/MindSpore-scientific/code-14/tree/main/TRSTMI

Trust Region Policy Optimization

https://github.com/MindSpore-scientific/code-2/tree/main/TRSTMI

Trust Region Policy Optimization

https://github.com/MindSpore-scientific/code-5/tree/main/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/Minhchuyentoancbn/Continual-Learning

Overcoming catastrophic forgetting in neural networks

https://github.com/MohammadAsadolahi/DDQN_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Deep Reinforcement Learning with Double Q-learning

https://github.com/MohammadAsadolahi/Dueling_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/MohammadAsadolahi/Gradient-Free-Reinforcement-Learning-solving-Openai-gym-LunarLanderV2-by-Evolution-Strategy-python/blob/main/README.md

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Montherapy/Deep-reinforcement-learning-for-multi-class-imbalanced-classification

Deep Reinforcement Learning with Double Q-learning

https://github.com/MrDaubinet/collaboration-and-competition

Prioritized Experience Replay

https://github.com/N0r9st/a2c-jax

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Narsil/rl-baselines

Exploration by Random Network Distillation

https://github.com/Nasdin/ReinforcementLearning-AtariGame

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NervanaSystems/coach

Playing Atari with Deep Reinforcement Learning

https://github.com/NikolausBerl/Udacity_DRLN_Navigation_Project

Deep Reinforcement Learning with Double Q-learning

https://github.com/Nkluge-correa/teeny-tiny_castle

Agent57: Outperforming the Atari Human Benchmark

https://github.com/OMS1996/Carla_The_RL_Self-Driving-Car

Deep Reinforcement Learning with Double Q-learning

https://github.com/PCJohn/cata-forget

Overcoming catastrophic forgetting in neural networks

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