강화 학습

수학노트
둘러보기로 가기 검색하러 가기

노트

  1. Reinforcement learning Supervised learning Reinforcement learning is all about making decisions sequentially.[1]
  2. In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism.[2]
  3. Using reinforcement learning, AlphaGo Zero was able to learn the game of Go from scratch.[2]
  4. In this paper, the authors propose real-time bidding with multi-agent reinforcement learning.[2]
  5. Reinforcement Learning is one of the hottest research topics currently and its popularity is only growing day by day.[3]
  6. As compared to unsupervised learning, reinforcement learning is different in terms of goals.[3]
  7. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce.[4]
  8. Thus, video games provide the sterile environment of the lab, where ideas about reinforcement learning can be tested.[4]
  9. But convolutional networks derive different interpretations from images in reinforcement learning than in supervised learning.[4]
  10. At the beginning of reinforcement learning, the neural network coefficients may be initialized stochastically, or randomly.[4]
  11. This article pursues to highlight in a non-exhaustive manner the main type of algorithms used for reinforcement learning (RL).[5]
  12. Reinforcement learning is the training of machine learning models to make a sequence of decisions.[6]
  13. In reinforcement learning, an artificial intelligence faces a game-like situation.[6]
  14. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity.[6]
  15. Training the models that control autonomous cars is an excellent example of a potential application of reinforcement learning.[6]
  16. Reinforcement learning (RL) is learning by interacting with an environment.[7]
  17. Reinforcement learning is also reflected at the level of neuronal sub-systems or even at the level of single neurons.[7]
  18. However, only few dopaminergic neurons produce error signals that comply with the demands of reinforcement learning.[7]
  19. In this post, we are gonna briefly go over the field of Reinforcement Learning (RL), from fundamental concepts to classic algorithms.[8]
  20. After knowing these, it is pretty hard not to be curious about the magic behind these algorithms — Reinforcement Learning (RL).[8]
  21. Dueling network architectures for deep reinforcement learning.[8]
  22. You need to remember that Reinforcement Learning is computing-heavy and time-consuming.[9]
  23. Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off.[10]
  24. In inverse reinforcement learning (IRL), no reward function is given.[10]
  25. This page contains Reinforcement Learning glossary terms.[11]
  26. In reinforcement learning, the world that contains the agent and allows the agent to observe that world's state.[11]
  27. In reinforcement learning, a policy that either follows a random policy with epsilon probability or a greedy policy otherwise.[11]
  28. In reinforcement learning, a DQN technique used to reduce temporal correlations in training data.[11]
  29. This paradigm of learning by trial-and-error, solely from rewards or punishments, is known as reinforcement learning (RL).[12]
  30. Two years ago we introduced the first widely successful algorithm for deep reinforcement learning.[12]
  31. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms.[13]
  32. The final chapter discusses the future societal impacts of reinforcement learning.[13]
  33. Reinforcement learning is the study of decision making over time with consequences.[14]

소스

메타데이터

위키데이터