Problem Statement

Develop a reinforcement learning agent using dynamic programming methods to solve the Dice game optimally. The agent will learn the optimal policy by iteratively evaluating and improving its strategy based on the state$-$value function and the Bellman equations.

Scenario:

A player rolls a $6-$sided die with the objective of reaching a score of exactly $100.$ On each turn, the player can choose to stop and keep their current score or continue rolling the die. If the player rolls a $1,$ they lose all points accumulated in that turn and the turn ends. If the player rolls any other number $(2-6),$ that number is added to their score for that turn. The game ends when the player decides to stop and keep their score OR when the player's score reaches $100.$ The player wins if they reach a score of exactly $100,$ and loses if they roll a $1$ when their score is below $100.$

#Environment Details

The environment consists of a player who can choose to either roll a $6-$sided die or stop at any point.

The player starts with an initial score $($e$.$g$.,0)$ and aims to reach a score of exactly $100.$

If the player rolls a $1,$ they lose all points accumulated in that turn and the turn ends. If they roll any other number $(2-6),$ that number is added to their score for that turn.

The goal is to accumulate a total of exactly $100$ points to win, or to stop the game before reaching $100$ points.

States

State s: Represents the current score of the player, ranging from $0$ to $100.$

Terminal States:

State s $=100$: Represents the player winning the game by reaching the goal of $100$ points.

State s $=0$: Represents the player losing all points accumulated in the turn due to rolling a $1.$

Actions

Action a: Represents the decision to either "roll" the die or "stop" the game at the current score.

The possible actions in any state s are either "roll" or "stop".

Expected Outcomes:

Use dynamic programming methods value iteration, policy improvement and policy evaluation to find the optimal policy for the Dice Game.

Implement an epsilon$-$greedy policy for action selection during training to balance exploration and exploitation.

Evaluate the agent's performance in terms of the probability of reaching exactly $100$ points after learning the optimal policy.

Use the agent's policy as the best strategy for different betting scenarios within the problem.

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