## Statistical Reinforcement Learning: Modern Machine Learning ApproachesReinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. - Covers the range of reinforcement learning algorithms from a modern perspective
- Lays out the associated optimization problems for each reinforcement learning scenario covered
- Provides thought-provoking statistical treatment of reinforcement learning algorithms
The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. |

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Statistical Reinforcement Learning: Modern Machine Learning Approaches Masashi Sugiyama No preview available - 2015 |