Design a set of states, rewards, and rules for an intelligent agent playing a simple board game. You may choose any board game that you are familiar with. In your design, be sure to consider the following: States, including starting and ending states, and possible actions Rewards or penalties for reaching a state Rules for navigating from one state to another Compare your approach to the Markov Decision Process (MDP) that you learned about in this module. What similarities and differences do you see between your approach and the MDP?

Reinforcement Learning (RL):Reinforcement Learning (RL) is a type of machine learning paradigm where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or punishments based on the actions it takes, and its objective is to learn a strategy or policy that maximizes the cumulative reward over time. RL is inspired by behavioral psychology, where the learning process is driven by the consequences of actions.Here are key components and concepts of reinforcement learning:Agent:The entity that takes actions in the environment. It is the decision-maker in the RL system.Environment:The external system with which the agent interacts. It can be anything from a physical environment (like a robot navigating a room) to a virtual environment (like a computer-simulated game).State:A representation of the current situation or configuration of the environment. The agent's decision-making is often based on the current state.Action:The set of possible moves or decisions that the agent can take in a given state. Actions influence the environment and lead to state transitions.Reward:A numerical value that the agent receives as feedback after taking an action in a particular state. The goal of the agent is to learn a policy that maximizes the cumulative reward over time.Policy:A strategy or mapping from states to actions that the agent follows. The policy defines the agent's behavior.Value Function:A function that estimates the expected cumulative reward of being in a certain state or taking a certain action. It helps the agent evaluate the desirability of different states or actions.Exploration and Exploitation:Balancing the exploration of new actions and the exploitation of known actions is crucial in RL. The agent needs to discover the environment and learn optimal strategies.Discount Factor:A parameter that determines the importance of future rewards. It helps in managing the trade-off between immediate rewards and long-term gains.Markov Decision Process (MDP):A mathematical framework that formalizes the RL problem. It consists of states, actions, transition probabilities, rewards, and a discount factor.Algorithms like Q-learning, Deep Q Network (DQN), and Policy Gradient methods are commonly used in RL to train agents to perform tasks and make decisions in complex environments.Let's design a simple board game that resembles Tic-Tac-Toe, a game most people are familiar with. We'll call it "Symbol Connect." The rules are as follows:Symbol Connect Game Rules:Board Representation:The board is a 3x3 grid.Each cell can be empty or filled with either an 'X' or an 'O'.Starting State:The board is empty.Ending States:1. Winning State:A player forms a line (horizontal, vertical, or diagonal) with three of their symbols in a row.Player 'X' wins or Player 'O' wins.2. Draw State:The board is completely filled, and no player has won.Possible Actions:Players take turns to place their symbol ('X' or 'O') in an empty cell on the board.Rewards/Penalties:1. Winning:+10 for the player who wins.-10 for the player who loses.2.Draw:+1 for each player in case of a draw.3. Illegal Move:-1 for attempting to place a symbol in a non-empty cell.Transition Rules:Players take turns to make legal moves.After a move, check for a winning state or a draw.If the move is illegal, the player loses their turn, and a penalty is applied.Markov Decision Process (MDP) Comparison:Symbol Connect gameMDPStates:States are represented by the current configuration of the board.States represent the situation of the system at a specific point.Actions:Actions in Symbol Connect involve placing a symbol on the board.Actions represent the decisions made by the agent.Rewards/Penalties:Symbol Connect assigns rewards or penalties based on the outcome of the game (win, draw, illegal move).MDP involves rewards associated with the transitions between states and the actions taken by the agent.Transition Rules:Symbol Connect has well-defined transition rules that determine how the system progresses from one state to another.MDP also has well-defined transition rules that determine how the system progresses from one state to another.Differences:Symbol Connect has a fixed grid size (3x3), and a clear terminal stateWhereas MDP can handle variable state and action spaces, some MDP problems might not have a definitive end.Symbol Connect is a simplified example of a board game, and its design shares some similarities with the concepts of an MDP. Both involve states, actions, and rewards, but the scope and complexity of MDPs can be much broader, covering a wide range of real-world problems beyond a simple board game.

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Database System Concepts

7th Edition

ISBN: 9780078022159

Author: Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher: McGraw-Hill Education

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Intelligent Machines

Machines that use artificial intelligence to learn from the environment and past experiences to perform better in the presence of variability and uncertainty are called intelligent machines. Intelligence develops according to environment and experience. …

Question

Design a set of states, rewards, and rules for an intelligent agent playing a simple board game. You may choose any board game that you are familiar with. In your design, be sure to consider the following:
- States, including starting and ending states, and possible actions
- Rewards or penalties for reaching a state
- Rules for navigating from one state to another
Compare your approach to the Markov Decision Process (MDP) that you learned about in this module. What similarities and differences do you see between your approach and the MDP?

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