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/ScottJordan/SafePolicyImprovementNonstationary
https://github.com/SeolhwaLee/ParlAI_blender_persona
https://github.com/SergiPonsa/Reinforcement-Learning-Sergi
https://github.com/SeungeonBaek/continuous-agents-test
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/SeungeonBaek/discrete-agents-test
Distributional Reinforcement Learning with Quantile Regression
https://github.com/SeungeonBaek/hybrid-agents-test
https://github.com/ShangtongZhang/DistributedES
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
https://github.com/ShaojieJiang/FACE_orig
https://github.com/ShaojieJiang/tldr
https://github.com/ShashwatNigam99/MARBLER
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/ShawK91/Evolutionary-Reinforcement-Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ShawnBLYU/offline_rl_envs
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
https://github.com/Sheepsody/Batched-Impala-PyTorch
https://github.com/Sheldonmao/Learning-To-walk
https://github.com/ShibiHe/Model-Free-Episodic-Control
https://github.com/ShibiHe/Q-Optimality-Tightening
https://github.com/Shikib/structured_fusion_networks
https://github.com/ShivamShrirao/deep_Q_learning
https://github.com/ShivamShrirao/deep_Q_learning_from_scratch
https://github.com/Shunichi09/PythonLinearNonlinearControl
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/Sidus-smg/ataxx-ai
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/SihongHo/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/SimarKareer/legged_gym
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
https://github.com/SimonRamstedt/ddpg
https://github.com/SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/SintolRTOS/multi-agent_Example
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/SmartSystems-UniAndes/ML_for_Solar_PV_Systems
https://github.com/SnDehghani/pcgrl_d_m
PCGRL: Procedural Content Generation via Reinforcement Learning
https://github.com/Sohojoe/ActiveRagdollAssaultCourse
https://github.com/Sohojoe/ActiveRagdollStyleTransfer
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/Somya-Patra/Shell_ai
https://github.com/Sooram/cnn
https://github.com/Sooram/stn
https://github.com/Souphis/mobile_robot_rl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.