Reinforcement learning | Wikipedia audio article
Channel: wikipedia tts
Date: 04/10/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Reinforcement_learning


00:00:59 1 Introduction
00:06:46 2 Exploration
00:08:03 3 Algorithms for control learning
00:08:24 3.1 Criterion of optimality
00:08:34 3.1.1 Policy
00:09:38 3.1.2 State-value function
00:12:46 3.2 Brute force
00:13:31 3.3 Value function
00:19:28 3.3.1 Monte Carlo methods
00:22:09 3.3.2 Temporal difference methods
00:25:47 3.4 Direct policy search
00:28:12 4 Theory
00:29:04 5 Research
00:30:34 6 Comparison of reinforcement learning algorithms
00:30:45 6.1 Deep reinforcement learning
00:31:10 6.2 Inverse reinforcement learning
00:31:35 6.3 Apprenticeship learning



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- Socrates


SUMMARY
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Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
It differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
The environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical dynamic programming methods  and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.