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/Zhiwei-Z/PrompLimitTest
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/Zhiwei-Z/SeqPromp
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/Zhiwei-Z/prompzzw
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/ZhuFengdaaa/minos
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
https://github.com/ZifanWu/Coordinated-PPO
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/ZiyangY/IndProject-RL-in-Supply-chain
https://github.com/ZiyiLiubird/DeepMARL-PyTorch
Distributional Reinforcement Learning with Quantile Regression
https://github.com/ZiyiZhang27/tdpo
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
https://github.com/Zorrorulz/MultiAgentDDPG-Tennis
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/a-ozeki/openAI_gym
https://github.com/aabbeell/reinforcementLearning.a2c.gym
https://github.com/aadeshnpn/Temporal-Credit-Assignment
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
https://github.com/aalmuzairee/dmcgb2
Generalization in Reinforcement Learning by Soft Data Augmentation
https://github.com/aaron-nguyen/DDPG-UAV-Efficiency
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement Learning
https://github.com/abalakrishna123/recovery-rl
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
https://github.com/abbadka/quadcopter
https://github.com/abcp4/DAPytorch
https://github.com/abcp4/MyDarts
https://github.com/abdulhaim/moe_options
https://github.com/abdulhaim/options_pytorch_image_env
https://github.com/abdullah-al-masud/rl-optimize
Neural Combinatorial Optimization with Reinforcement Learning
https://github.com/abefetterman/hamstir-gym
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
https://github.com/abhigoyal1997/CS-763-Project
https://github.com/abhiksingla/gym
https://github.com/abhishekchugh17/sql12
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/abhishekunique/RND-ashwin
End-to-End Robotic Reinforcement Learning without Reward Engineering
https://github.com/abhshkdz/House3D
https://github.com/abnerlijin/Strategy
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/abryeemessi/Wednesday
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/ac-93/soft-actor-critic
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ac-93/tactile_gym_servo_control
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation
https://github.com/acht7111020/DRL-playing-2.5D-fighting-game
https://github.com/activatedgeek/marl-challenges-approaches
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches
https://github.com/acutronicrobotics/ros2learn
https://github.com/acyclics/MPO
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.