Deep Reinforcement Learning $---$ Please answer all question

Assignment $01$ Problem Statement

$13$ Marks

Title: Propose a suitable title

Problem Statement: Define a problem statement of your own with a well$-$defined objective, gaming environment, and game controls. $[1$ Mark$]$

Concept Sketch: A pen and paper$-$based game concept sketching to illustrate the proposed gaming problem statement. $[1$ Mark$]$

Additional Information: Provide any necessary information assumed$/$considered for the game implementation.

Requirements and Deliverables:

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Elaborate on how the described problem could be solved using deep neural network and explain the action plan to create a gaming environment. $[1$ Mark$]$

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Prepare a Colab sheet with outputs saved satisfying the following requirements. Implementation should be in OpenAI gym with python. Develop a deep neural network architecture and training procedure that effectively learns the optimal policy for the spaceship to avoid collisions with asteroids and maximize its survival time in the game environment.

i$.$

Environment Setup: Define the game environment, including the state space, action space, rewards, and terminal conditions. $[1\mathrm{.}5$ Mark$]$

ii$.$

Replay Buffer: Implement a replay buffer to store experiences $($state$,$ action, reward, next state, terminal flag$).[1\mathrm{.}5$ Mark$]$

iii.

Deep Q$-$Network Architecture: Design the neural network architecture for the DQN using Convolutional Neural Networks. The input to the network is the game state, and the output is the Q$-$values for each possible action. $[2$ Marks$]$

iv$.$

Epsilon$-$Greedy Exploration: Implement an exploration strategy such as epsilon$-$greedy to balance exploration $($trying new actions$)$ and exploitation $($using learned knowledge$).[1$ Mark$]$

v$.$

Training Loop: Initialize the DQN and the target network $($a separate network used to stabilize training$).$ In each episode, reset the environment and observe the initial state. $[2$ Marks$]$

vi$.$

Testing and Evaluation: After training, evaluate the DQN by running it in the environment without exploration $($set epsilon to $0).$ Monitor metrics such as average reward per episode, survival time, etc., to assess the performance. $[2$ Mark$]$

Please provide the complete code based solution.