Reinforcement Learning

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Presentation transcript:

Reinforcement Learning 2018-02-04

Overview Tabular Methods Approximate Methods Deep Reinforcement Learning

Tabular Methods

Model: Mathematical models of dynamics and reward Policy: function mapping agent’s states to action Value function: future rewards from being in a state and/or action when following a particular policy

MDP

Markov Reward Process

Markov Reward Process

MDP = MRP + Action

MDP + Policy

Compare

How to Control?

Policy Search

State-Action Value Q

Policy Iteration

Worst Case Policy Iteration Can Take At Most |A|^|S| Iterations* (Size of # Policies)

Value Iteration