https://github.com/dragen1860/Reptile-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/drblallo/open_spiel
https://github.com/drduda/ReAllY
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
https://github.com/drforester/Q-learning-Intersection-Crossing
https://github.com/drozzy/upsidedown
Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions
https://github.com/dsapandora/s_cera
https://github.com/dsbrown1331/safe-imitation-learning
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
https://github.com/dsgiitr/rl_2048
https://github.com/dsinghnegi/atari_RL_agent
https://github.com/dtabas/epymarl
https://github.com/dtabas/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/dtak/hip-mdp-public
https://github.com/dti-research/SenseAct
Setting up a Reinforcement Learning Task with a Real-World Robot
https://github.com/durashi/Dialog_policy_network_for_low_resource_setting
https://github.com/dxyang/DQN_pytorch
https://github.com/dylancashman/remap_nas
Designing Neural Network Architectures using Reinforcement Learning
https://github.com/dyth/doublegum
https://github.com/e155763/GAN-RL
Surprising Negative Results for Generative Adversarial Tree Search
https://github.com/eac-replication/eac-replication
https://github.com/ebadrian/metadl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/eddynelson/dqn
https://github.com/edlanglois/relearn
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/edmundmills/basalt-competition
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
https://github.com/eduardosebastianrodriguez/phmarl
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/edwardwardward/crypto_ml
Cryptocurrency Portfolio Management with Deep Reinforcement Learning
https://github.com/efancher/cs234_work_final_project
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
https://github.com/efg59/Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/egiob/diversityisallyouneed-sb3
Diversity is All You Need: Learning Skills without a Reward Function
https://github.com/egipcy/LIRD
https://github.com/eikonal-equation/Bayesian-Surveillance-Evasion
Surveillance Evasion Through Bayesian Reinforcement Learning
https://github.com/eivindeb/gym-letMPC
Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning
https://github.com/elicassion/3dtrl
Time-Contrastive Networks: Self-Supervised Learning from Video
https://github.com/elitalobo/Hierarchical-RL-Algorithms
https://github.com/elytopia/Info-Sec-Hammerer-and-Brunori
Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger
https://github.com/emerge-lab/nocturne_lab
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/emi-group/evox
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.