Slot Machine Markov Decision Process

Markov Decision Process Application
Slot Machine Markov Decision Process Involves
Slot Machine Markov Decision Process Definition

In this paper, we model the scheduling of a node-wise sequential LDPC decoder as a Markov decision process, where the underlying Tanner graph is viewed as a slot machine with multiple arms corresponding to the check nodes. A fictitious gambler decides which check node to pull (schedule) next by observing a reward associated with each pull. Markov Decision Process Operations Research Artificial Intelligence Machine Learning Graph Theory. Most often studied in machine learning, economics, operations. It was believed that the distribution of slot machines were controlled by organized crime and legislation restricted their sale. Nevada was the only state where legalized gambling was allowed. As other states and countries permitted gambling, the slot machines.

Reinforcement Learning :

Reinforcement Learning is a type of Machine Learning. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal.

There are many different algorithms that tackle this issue. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed to decide the best action to select based on his current state. When this step is repeated, the problem is known as a Markov Decision Process.

A Markov Decision Process (MDP) model contains:

A set of possible world states S.
A set of Models.
A set of possible actions A.
A real valued reward function R(s,a).
A policy the solution of Markov Decision Process.

What is a State?

A State is a set of tokens that represent every state that the agent can be in.

What is a Model?

A Model (sometimes called Transition Model) gives an action’s effect in a state. In particular, T(S, a, S’) defines a transition T where being in state S and taking an action ‘a’ takes us to state S’ (S and S’ may be same). For stochastic actions (noisy, non-deterministic) we also define a probability P(S’|S,a) which represents the probability of reaching a state S’ if action ‘a’ is taken in state S. Note Markov property states that the effects of an action taken in a state depend only on that state and not on the prior history.

What is Actions?

An Action A is set of all possible actions. A(s) defines the set of actions that can be taken being in state S.

Markov Decision Process Application

What is a Reward?

A Reward is a real-valued reward function. R(s) indicates the reward for simply being in the state S. R(S,a) indicates the reward for being in a state S and taking an action ‘a’. R(S,a,S’) indicates the reward for being in a state S, taking an action ‘a’ and ending up in a state S’.

What is a Policy?

A Policy is a solution to the Markov Decision Process. A policy is a mapping from S to a. It indicates the action ‘a’ to be taken while in state S.

Let us take the example of a grid world:

An agent lives in the grid. The above example is a 3*4 grid. The grid has a START state(grid no 1,1). The purpose of the agent is to wander around the grid to finally reach the Blue Diamond (grid no 4,3). Under all circumstances, the agent should avoid the Fire grid (orange color, grid no 4,2). Also the grid no 2,2 is a blocked grid, it acts like a wall hence the agent cannot enter it.

The agent can take any one of these actions: UP, DOWN, LEFT, RIGHT

Walls block the agent path, i.e., if there is a wall in the direction the agent would have taken, the agent stays in the same place. So for example, if the agent says LEFT in the START grid he would stay put in the START grid.