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/alexsax/testing_rlpyt
https://github.com/alextrudeau/go_exploit
https://github.com/alexub/jax-meta-learning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/alhabk/SGEE--pytorch
https://github.com/alielhassouni/heartsteps-gaussian-generative-model
Group-driven Reinforcement Learning for Personalized mHealth Intervention
https://github.com/aliengirlliv/teachable
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/alirezakazemipour/DIAYN-PyTorch
Diversity is All You Need: Learning Skills without a Reward Function
https://github.com/alirezakazemipour/ppo-rnd
https://github.com/alisidd/Evolution-Strategies
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
https://github.com/allanpichardo/dontpanic
Accuracy-based Curriculum Learning in Deep Reinforcement Learning
https://github.com/alpaficia/MOSEAC_Limo
https://github.com/alphadl/darts.pytorch1.1
https://github.com/alreadydone/Leela_Phoenix
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/alt113/flow-triangle-scenario
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/aluscher/torchbeastpopart
https://github.com/amaar-quadri/PerfectInformationGame
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/amaarquadri/PerfectInformationGame
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/aman-khurana/deep-q-learning
https://github.com/amanda-lambda/drl-experiments
https://github.com/amanda-lambda/hack-flappy-bird-drl
https://github.com/amaudruz/RL_openaigym
https://github.com/ameet-1997/Prioritized_Experience_Replay
https://github.com/amidos2006/marahel
PCGRL: Procedural Content Generation via Reinforcement Learning
https://github.com/amir-samadi/safe-rl
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
https://github.com/amirmirzaei79/CartPole-DQN-And-DDQN
https://github.com/amjshl/compute_efficient_alphazero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/amobiny/Recurrent_Attention_Model
https://github.com/amzoyang/CS-221-Final-Project
Rainbow: Combining Improvements in Deep Reinforcement Learning
https://github.com/ana-tudor/classyconditioning
Leveraging Procedural Generation to Benchmark Reinforcement Learning
https://github.com/anagabandi/nn_dynamics
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/anair13/bullet-manipulation-affordances
Offline Meta-Reinforcement Learning with Online Self-Supervision
https://github.com/anair13/rlkit/tree/ccrig/examples/ccrig
Contextual Imagined Goals for Self-Supervised Robotic Learning
https://github.com/andreaslillevangbech/PortfolioManager-pytorch
Cryptocurrency Portfolio Management with Deep Reinforcement Learning
https://github.com/andreidi/AC_DDPG_walker
https://github.com/andrewbo29/mtm-meta-learning-sa
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.