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/madeleinesnyder/Subbat
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
https://github.com/madhur-tandon/RL-Project
https://github.com/madras-simulator/Multi-Agent-Particle-Environment
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/madvn/DDPG
https://github.com/magister-informatica-uach/INFO267
https://github.com/magnusja/ppo
https://github.com/mahi97/XQMIX
https://github.com/mahshidhelali/RL-Assisted-Performance-Testing
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
https://github.com/majercakdavid/gym-virtual-quant-trading
https://github.com/makeyourownmaker/mixup
Regularizing Neural Networks by Penalizing Confident Output Distributions
https://github.com/malashinroman/least_action
Sparsely ensembled convolutional neural network classifiers via reinforcement learning
https://github.com/malerinc/fabricatio-rl
Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems
https://github.com/manantomar/video-occupancy-models
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
https://github.com/mangushev/inventory_management
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains
https://github.com/manhdung20112000/RL-Image-Classification
https://github.com/manshri/tesum
https://github.com/manvibharat/Stock-price-pridiction-using-Deep-reienforcement-learning
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/maraghuram/I-DQN
https://github.com/mari-linhares/tensorflow-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/mariuspot/openai_gym_taxi_v2_sarsa_max
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
https://github.com/mark-gluzman/NmodelPPO
https://github.com/markWJJ/TC-Bot
https://github.com/markub3327/rl-agent
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/markub3327/rl-baselines
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/markub3327/rl-toolkit
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/markusdutschke/yahtzee
https://github.com/marload/DeepRL-TensorFlow2
https://github.com/marload/DistRL-TensorFlow2
https://github.com/marload/deep-rl-tf2
https://github.com/marload/dist-rl-tf2
https://github.com/martius-lab/beta-nll
Causal Influence Detection for Improving Efficiency in Reinforcement Learning
https://github.com/marusan03/GAN-RL
Surprising Negative Results for Generative Adversarial Tree Search
https://github.com/marwanihab/RL_Tag_Game
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/marwanihab/RL_Testing_Noise_ASRN
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/massquantity/DBRL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.