420.420. Deep Q-Network (DQN)

Deep Q-Network (DQN)

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In this problem, you will implement a Deep Q-Network (DQN), a type of Deep Reinforcement Learning algorithm used to approximate the action-value function (Q-function).

**Example:*

Suppose we have a simple Markov Decision Process (MDP) with two states (A and B) and two actions (left and right). The agent starts at state A and receives a reward of -1 for each step. The goal is to reach state B.

| State | Action | Next State | Reward |

| --- | --- | --- | --- |

| A | left | A | -1 |

| A | right | B | 10 |

| B | left | B | -1 |

| B | right | A | -1 |

The agent starts at state A and chooses the right action, reaching state B. The reward is 10, which is higher than the expected reward of -1. This experience should be used to update the Q-function.

Constraints: