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/sahandrez/distracting_control
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels
https://github.com/sahandrez/gym_forestfire
https://github.com/saikrishna-1996/deep_pepper_chess
https://github.com/sainijagjit/A3C-Pytorch
https://github.com/salesforce/codet5
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
https://github.com/samadiamir/IE2E
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
https://github.com/samhippie/q-game-agent
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/samiamkhan/4641Project
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/samiranrl/ODSC_2019_RL
https://github.com/samuel500/World-Models
https://github.com/samuelfneumann/GoAtar
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
https://github.com/samuelmat19/DDPG-tf2
https://github.com/sannebh/riskaverse
Distributional Reinforcement Learning with Quantile Regression
https://github.com/sannebh/riskaverse_rl
Distributional Reinforcement Learning with Quantile Regression
https://github.com/saoudh/Reinforcement-Learning
https://github.com/sapanachaudhary/pytorch-cpo
https://github.com/sarahisyoung/rlpyt
https://github.com/sarthakTUM/progressive-neural-networks-for-nlp
https://github.com/saschaschramm/Pong
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/sauxpa/ML_101
https://github.com/sauxpa/neural_exploration
Provably Efficient Reinforcement Learning with Linear Function Approximation
https://github.com/saxena-mayur/Weight-Uncertainty-in-Neural-Networks
https://github.com/sayantanauddy/hierarchical_bipedal_controller
https://github.com/sb-ai-lab/RePlay
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
https://github.com/sbieringer/classificationsurrogates
https://github.com/sbodenstein/Tutorials
Simple random search provides a competitive approach to reinforcement learning
https://github.com/schatty/D4PG-pytorch
https://github.com/schrammlb2/USHER_Implementation
https://github.com/schroederdewitt/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/scottemmons/youngs-cql
https://github.com/scy6500/blenderbot2-client
https://github.com/scz233/darts
https://github.com/sdicastro/simandrealbettertogether
https://github.com/sea-snell/implicit-language-q-learning
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/seacevedo/ReinforcementLearningProjects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.