https://github.com/hydesmondliu/b2rl
B2RL: An open-source Dataset for Building Batch Reinforcement Learning
Papers with Code 아카이브
Reinforcement Learning (RL)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 2
- 보존된 평가 기록
- 3
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/hyecheol123/Summary_of_QT-Opt
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
https://github.com/hyerinshelly/RL_COVID-19_Korea
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
https://github.com/hysonlab/learnmmf
Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs
https://github.com/hyunghona/emu
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
https://github.com/hyunin-lee/ForecasterSAC
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/hzwer/LearningToPaint
Learning to Paint With Model-based Deep Reinforcement Learning
https://github.com/hzy312/knowledge-r1
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent
https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient
https://github.com/iDataist/Navigation-with-Deep-Q-Network
https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement
https://github.com/iSarmad/RL-GAN-Net
https://github.com/ianchar/gpide
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
https://github.com/ianlimle/ItsMeMario
https://github.com/iassael/learning-to-communicate
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
https://github.com/iclavera/cassie
https://github.com/iclavera/learning_to_adapt
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/iclavera/meta-mb
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/icmlanon58443043/vizdoom
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
https://github.com/icmlanon58443043/vizdoomicmlanon
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
https://github.com/ido90/roml-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ido90/roml-pearl
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://github.com/idthanm/env_build
https://github.com/idthanm/mpg
Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model
https://github.com/ifestus/rl
https://github.com/iffiX/PGPPortfolio-pytorch
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/iffiX/PGPortfolio-pytorch
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/ifpen/wfcrl-benchmark
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/iglu-contest/nlp-baselines-2022
https://github.com/igoracmorais/inteligencia_artificial
https://github.com/ikostrikov/cql-results
https://github.com/ikostrikov/jax-rl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ikostrikov/pytorch-a3c
https://github.com/ikostrikov/pytorch-rl
https://github.com/ilBarbara/BioRL
Exploratory Combinatorial Optimization with Reinforcement Learning
https://github.com/illidanlab/sail
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.