Markov Decision Processes Four - Georgia Tech - Machine Learning
Udacity
6 min, 53 sec
The video explains the concept of policies in the context of Markov Decision Processes (MDPs) and how they lead to optimal solutions.
Summary
- A policy is a function that takes a state and returns an action to achieve the best long-term expected reward.
- The optimal policy (policy star) is the one that maximizes the long-term expected reward across all states.
- Policies differ from plans, as they specify actions for each state rather than a fixed sequence of actions.
- The video also touches on the differences between reinforcement learning and other types of learning like supervised learning.
- The focus shifts towards how to find the optimal policy given a defined MDP.
Chapter 1
The video begins with an introduction to the solution of Markov Decision Processes, which is a policy, and explains its basic functionality.
- A policy is introduced as a fundamental solution to MDPs.
- The policy is a function mapping states to actions for any given state.
- An optimal policy maximizes long-term expected rewards.
Chapter 2
The concept of the optimal policy is further elaborated, highlighting its role in maximizing rewards in MDPs.
- The optimal policy, or policy star, is the one that maximizes the expected reward over time.
- The explanation clarifies that rewards can be received at any point in time, not just at the end.
Chapter 3
The discussion shifts to the differences between learning policies in MDPs and other learning paradigms.
- The conversation compares reinforcement learning to supervised learning.
- In MDPs, one observes states, actions, and the associated rewards, which is different from being given correct actions directly.
Chapter 4
The video delves into the nature of policies, emphasizing that a policy is simply a function that prescribes actions for each state.
- A policy provides a mapping for each state, indicating the action to be taken.
- It is clarified that policies prescribe actions based on the current state, not on a sequence of actions.
Chapter 5
The video explains how policies operate within the framework of MDPs and their advantage in handling stochastic environments.
- Policies guide actions to take in each state encountered, not a fixed sequence of actions.
- This approach is robust to the stochastic nature of the environment.
Chapter 6
The distinction between policies and plans is further clarified, highlighting how policies adapt to the state changes.
- Policies adapt to the state changes, while plans are a fixed sequence of actions.
- Policies are advantageous as they tell what to do in every possible state.
Chapter 7
The conversation seeks to clarify the nature of policies in contrast to the idea of a planned sequence of actions.
- Policies determine actions based on the current state, not the sequence of actions taken.
- The concept of a policy is contrasted with the idea of a pre-determined plan.
Chapter 8
The video concludes with a discussion on whether having an optimal policy is sufficient for the best behavior in MDPs.
- An optimal policy is sufficient for the best behavior in all situations within an MDP.
- The discussion covers the necessity and sufficiency of policies for effective decision-making.