July 2017
Beginner to intermediate
442 pages
10h 8m
English
Content preview from Statistics for Machine Learning
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Characteristics of reinforcement learning
- The feedback of a reward signal is not instantaneous. It is delayed by many timesteps
- Sequential decision making is needed to reach a goal, so time plays an important role in reinforcement problems (no IID assumption of the data holds good here)
- The agent's action affects the subsequent data it receives
In reinforcement learning, a little bit of supervision is needed, but much less supervision compared to supervised learning.
The following are a few actual live examples of reinforcement learning problems:
- Autonomous helicopter: The objective of autonomous helicopter is to change its roll, pitch and yaw to control its position by controlling the joystick, pedals, and so on. Sensors send inputs 10 ...
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