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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/emiliojorge/Inventing-a-Grounded-Language
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
https://github.com/eml-eda/plinio
https://github.com/emnlp-2021-g2r/ParlAI
https://github.com/emunaran/stochastic-human-driving-policies-drl
https://github.com/enajx/ES
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
https://github.com/enikhu/dreamer
https://github.com/enpasos/muzero
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/eparisotto/ActorMimic
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
https://github.com/epignatelli/human-level-control-through-deep-reinforcement-learning
https://github.com/epsilon-deltta/ssd_guillotine
https://github.com/ercumentilhan/MinAtar
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
https://github.com/erekgit/parlai
https://github.com/erfanMhi/awesome-continual-learning
https://github.com/erfanMhi/awesome-continual-representation-learning
https://github.com/eric-erki/robotics-rl-srl
Simple random search provides a competitive approach to reinforcement learning
https://github.com/eric-mitchell/macaw-min
Offline Meta-Reinforcement Learning with Advantage Weighting
https://github.com/eric-yim/fin_map
https://github.com/ericborn/binarybot
https://github.com/ericchen321/ros_x_habitat
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
https://github.com/erickrosete/tacorl
Latent Plans for Task-Agnostic Offline Reinforcement Learning
https://github.com/ermongroup/CalibratedModelBasedRL
https://github.com/ermongroup/EfficientObjectDetection
Efficient Object Detection in Large Images using Deep Reinforcement Learning
https://github.com/ermongroup/higher_order_invariance
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/ermongroup/multiagent-gail
https://github.com/erwincoumans/tds_experimental
https://github.com/esmeralday/MARL
https://github.com/esparza83/esparza83-udacity_rl_project3
https://github.com/eth-pbl/elmap-rl-controller
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
https://github.com/ethancaballero/neural-engineers-first-attempt
https://github.com/ethanluoyc/transporter-pytorch
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/ethanmclark1/carla_aebs
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/ethz-asl/rl-navigation
https://github.com/ethz-spylab/rlhf_trojan_competition
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
https://github.com/eublefar/dqn
https://github.com/eugenevinitsky/cdc_bottlenecks
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.