https://github.com/adamjelley/efficientofflinerl
Revisiting the Minimalist Approach to Offline Reinforcement Learning
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/adammenges/DARTS-PyTorch
https://github.com/adaptive-intelligent-robotics/pga-map-elites
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/adityabingi/Dreamer
https://github.com/adityagilra/archibrain
Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory
https://github.com/aduerig/burrus
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/adymaharana/DeepPath_PyTorch
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
https://github.com/aerialroboticsgroup/agile-tethered-perching
https://github.com/afengisme11/IAM-Reproduce
Influence-aware Memory Architectures for Deep Reinforcement Learning
https://github.com/afrakilic/btn-kernel-machines
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
https://github.com/agentica-project/verl-pipeline
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
https://github.com/agiachris/STAP
https://github.com/ahundt/sharpDARTS
https://github.com/ai-nikolai/bsuite
https://github.com/ai-winter/python_motion_planning
https://github.com/ai4co/camp
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
https://github.com/aigerimb/FFEVSS
A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems
https://github.com/ailab-pku/rl-framework
https://github.com/ailabdsunipi/pymarlzooplus
https://github.com/aimagelab/mammoth
https://github.com/ajayn1997/RL-VRP-PtrNtwrk
Reinforcement Learning for Solving the Vehicle Routing Problem
https://github.com/ajaysub110/rl-pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/akanazawa/motion_reconstruction
https://github.com/aksub99/MolDQN-pytorch
https://github.com/akterskii/rlpyt
https://github.com/alathiya/RL-Quadcoptor-Flying
https://github.com/albertwilcox/latent-space-safe-sets
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
https://github.com/alec-tschantz/vime
https://github.com/aleedelarica/XDRL-for-finance
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/alescontrela/viper
https://github.com/alessandrositta/Flatland_challenge
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/alexandonian/pretorched-x
https://github.com/alexdominguez09/pedestrian_direction
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/alexeypustynnikov/AM-VRP
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
https://github.com/alexis-jacq/numpy_ARS
Simple random search provides a competitive approach to reinforcement learning
https://github.com/alexlee-gk/slac
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.