# 강화 학습

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## 노트

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

### 소스

- ↑ Reinforcement learning
- ↑
^{2.0}^{2.1}^{2.2}10 Real-Life Applications of Reinforcement Learning - ↑
^{3.0}^{3.1}5 Things You Need to Know about Reinforcement Learning - ↑
^{4.0}^{4.1}^{4.2}^{4.3}A Beginner's Guide to Deep Reinforcement Learning - ↑ Reinforcement Learning algorithms — an intuitive overview
- ↑
^{6.0}^{6.1}^{6.2}^{6.3}What is reinforcement learning? The complete guide - ↑
^{7.0}^{7.1}^{7.2}Reinforcement learning - ↑
^{8.0}^{8.1}^{8.2}A (Long) Peek into Reinforcement Learning - ↑ Reinforcement Learning: What is, Algorithms, Applications, Example
- ↑
^{10.0}^{10.1}Reinforcement learning - ↑
^{11.0}^{11.1}^{11.2}^{11.3}Machine Learning Glossary: Reinforcement Learning - ↑
^{12.0}^{12.1}Deep Reinforcement Learning - ↑
^{13.0}^{13.1}Reinforcement Learning, Second Edition - ↑ Reinforcement Learning

## 메타데이터

### 위키데이터

- ID : Q830687