This Academic project implements a Deep Q-Network (DQN) to solve a continuous 2D maze navigation problem with complex reward structures and obstacles. Through a series of experiments—ranging from step-limit tuning to epsilon decay strategies and architectural improvements—the agent successfully learns both single and multi-path solutions to reach the goal, avoiding danger zones and optimizing rewards.

• State Space: 2D continuous position (x, y)
• Action Space: 4 discrete actions (up, down, left, right)
• Goal: Navigate from start to goal
• Obstacles: Walls and danger zones (red rectangles)
• Rewards:
  • Goal reached: +100
  • Danger zone: -10 (episode ends)
  • Wall collision: -1
  • Distance-based shaping: reward for moving closer to goal
  • Step penalty: -0.01 per step

• max_steps: 200
• epsilon: Exponential decay from 1.0 to 0.1
• Network: 4-layer architecture (256→256→128→4) with dropout
• Learning rate: 0.001 with gradient clipping
• Key Achievement: Agent learned multiple optimal paths to the goal
• Improvement: Enhanced exploration-exploitation balance with proper consecutive success tracking