drkostas / Minecraft-AI

A Reinforcement Learning agent that learns how to to solve maze missions in Minecraft.
Apache License 2.0
201 stars 8 forks source link

Dynamic Environment and Adaptive Learning Strategies #16

Open ghost opened 2 months ago

ghost commented 2 months ago

Hi,

The Minecraft AI project is a fantastic demonstration of reinforcement learning. I have a few advanced suggestions to enhance the agent's learning and adaptability:

Suggested Enhancements:

  1. Dynamic Environment:

    • Adaptive Obstacles: Introduce moving obstacles and changing environmental conditions (e.g., weather effects) to challenge the agent's adaptability.
    • Randomized Maze Layouts: Generate different maze configurations for each episode to prevent the agent from memorizing the layout.
  2. Advanced Reward Structure:

    • Hierarchical Rewards: Implement a multi-tiered reward system that includes intermediate checkpoints and sub-goals to guide the agent's progress.
    • Exploration Incentives: Provide rewards for exploring new areas to encourage thorough investigation of the maze.

3Adaptive Learning Strategies:

  1. Improved State Representations:
    • Augmented Visual Input: Include additional sensory inputs, such as depth perception or object recognition, to enhance the agent's understanding of the environment. -Feature Extraction: Use advanced techniques like convolutional neural networks (CNNs) to process raw pixel data more effectively.

Benefits:

Thank you for considering these suggestions to take the project to the next level!

Ruby Poddar

drkostas commented 1 month ago

Hi @rubypoddar , do you want to work on any of those enhancements?