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

https://github.com/patniemeyer/ga-autoencoder

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/pauldebryas/SND_trackers_My_data

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/pavitrakumar78/Playing-custom-games-using-Deep-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/pereirarodrigo/project_spike

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/pereirarodrigo/spike

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/pervasive-ai-lab/nle-language-wrapper

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/petuum/adaptdl

An Empirical Model of Large-Batch Training

https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code

Deep Reinforcement Learning with Double Q-learning

https://github.com/pihey1995/DistributionalRL

A Distributional Perspective on Reinforcement Learning

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

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/pku-marl/harl

Maximum Entropy Heterogeneous-Agent Reinforcement Learning

https://github.com/pku-rl/copl

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

https://github.com/pocokhc/agent57

Agent57: Outperforming the Atari Human Benchmark

https://github.com/prajwalgatti/DRL-Continuous-Control

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/prajwalgatti/DRL-Navigation

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/prasoongoyal/rl-learn

Using Natural Language for Reward Shaping in Reinforcement Learning

https://github.com/proroklab/popgym

Playing Atari with Deep Reinforcement Learning

https://github.com/pskugit/custom-conv2d

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/puppetect/TradingBot-tensorflow

Deep Reinforcement Learning with Double Q-learning

https://github.com/pwc-1/Paper-8/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/a2c

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/ppo

Proximal Policy Optimization Algorithms

https://github.com/qgallouedec/deep_rl

A Distributional Perspective on Reinforcement Learning

https://github.com/qiankun214/DQN-FlappyBird-python3

Playing Atari with Deep Reinforcement Learning

https://github.com/qihongl/demo-advantage-actor-critic

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/qihongl/dlstm-demo

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/raccoon831012/StartCraft2-RL

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/rasmusbergpalm/evostrat

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/ray-project/ray/tree/master/rllib

Playing Atari with Deep Reinforcement Learning

https://github.com/rddy/ReQueST

Reward learning from human preferences and demonstrations in Atari

https://github.com/reinforcement-learning-kr/pg_travel

Proximal Policy Optimization Algorithms

https://github.com/rejuvyesh/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rgreenblatt/ai_alignment_readings

Scalable agent alignment via reward modeling: a research direction

https://github.com/richardrl/cartpole-request-for-research

Benchmarking Deep Reinforcement Learning for Continuous Control

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