https://github.com/matejker/controllability-of-complex-networks
A Tour of Reinforcement Learning: The View from Continuous Control
Papers with Code 아카이브
Reinforcement Learning (RL)
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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mathformoso/RL-playground---ddpg
https://github.com/mathllm/math-v
https://github.com/matthewsparr/Deep-Zork
Language Understanding for Text-based Games Using Deep Reinforcement Learning
https://github.com/matthewsparr/Reinforcement-Learning-Lesson
https://github.com/matthias-mayr/skireil
https://github.com/matus-jan-lavko/ReinforcementLearning-vs-EW
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
https://github.com/mauricemager/multiagent-robot
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/mauriciogtec/cvi
https://github.com/mavischer/DRRL
https://github.com/max-schenke/DESSCA
https://github.com/maxiaoba/SoftLearning
https://github.com/maximecohen2/conception_solution_appli_ia_i4_tp_research
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
https://github.com/mayankk6196/darts-python
https://github.com/maziarg/PrivAttack-BCQ
https://github.com/mazm13/Image-to-Tree.pytorch
https://github.com/mazpie/mime
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
https://github.com/mbrucker07/experiments
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/mcederle99/MAD4QN-PS
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/mdabbah/pacman-rl-project
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
https://github.com/medtray/SeqGAN-vs-MLE-vs-PG-BLEU-vs-ScheduleSampling
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/meera1hahn/NRNS
No RL, No Simulation: Learning to Navigate without Navigating
https://github.com/mees/Adversarial-Skill-Networks
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video
https://github.com/megagonlabs/rotom
https://github.com/megvii-research/ICCV2019-LearningToPaint
Learning to Paint With Model-based Deep Reinforcement Learning
https://github.com/megvii-research/NIPS2017-LearningToRunACE
https://github.com/mehdimashayekhi/Some-RL-Implementation
https://github.com/mehdinasiri/mirror-descent-in-marl
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/mengyangbai/CchessGo
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/metataro/minerl_agent
Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft
https://github.com/metekemertas/RobustBisimulation
Learning Invariant Representations for Reinforcement Learning without Reconstruction
https://github.com/mf1832146/tree-transformer
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
https://github.com/mf1832146/tree_transformer_2.0
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
https://github.com/mfregeau/DeepLearning
https://github.com/mgbellemare/Arcade-Learning-Environment
The Arcade Learning Environment: An Evaluation Platform for General Agents
https://github.com/mgorkove/blender2_server
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.