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/fmu2/PyTorch-MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/foersterrobert/AlphaZero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/fomorians-oss/awr
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
https://github.com/foolyc/Meta-SGD
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/forevergj/multithreading_ppo_RND
https://github.com/forrestneo/FinRL-pytorch-tushare
Practical Deep Reinforcement Learning Approach for Stock Trading
https://github.com/francidellungo/Minigrid_HCI-project
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
https://github.com/frankhan91/DeepBSDE
https://github.com/franroldans/custom_gym
https://github.com/freedombenLiu/ParlAI
https://github.com/frgfm/Holocron
https://github.com/fshamshirdar/pytorch-rdpg
https://github.com/gabrielsantosrv/coco-caption
https://github.com/gaetanserre/lipo
https://github.com/gaetanserre/stochastic-global-optimization
https://github.com/ganjinzero/rrhf
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
https://github.com/garyzyr001/rethinking-airl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/gauthambekal93/hvac_continual_rl
https://github.com/gazealytics/gazealytics-master
Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
https://github.com/gbotev/PGPortfolio
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/gcomb/GCOMB
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
https://github.com/gcp/leela-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/gcucurull/maml_flax
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gd-goblin/C-3PO_Motion_Retargeting_Module
https://github.com/gd-goblin/C-3PO_RobotControlModule
https://github.com/gd-zhang/noisy-K-FAC
https://github.com/gebob19/REPTILE-Metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/cscd94-metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/cscd94_metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/geeky-wizard/Atari-Deep-Reinforcement-Learning
https://github.com/gengsinong/samq
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
https://github.com/gentopia-ai/gentopia
https://github.com/geohot/twitchcoq
https://github.com/georgesung/TD3
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/georgkruse/cleanqrl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.