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The Chosen One trained with Multi-Agent Reinforcement Learning and Generative Progressive Autocurricula, won Best AI Hack at iNTUition 2019.

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The Chosen One - Multi-Agent Reinforcement Learning with Autocurricula

Chosen One Main Pic

Introduction Video - Animator vs Animation by Alan Becker

https://www.youtube.com/watch?v=npTC6b5-yvM&t=60s

Contributions

  • PyGame environment, consisting Chosen One (agent), Gun and Bullets
  • Multi-agent Reinforcement Learning Training, consisting Chosen One (agent) and Generator (Gun)

Demonstration

Agent learnt to camp at a corner:

camp_clip

Agent learnt to dodge Gun bullets:

demo

Method

  • Trained over 1K epochs of 500 timesteps.
  • Chosen One
    • Model: 5 hidden layers, 100 hidden channels
    • Reward function: +1 if surviving, -50 if hit
    • Discount factor (g): 0.995
    • Exploration policy: Epsilon-greedy (e = 0.3)
    • Experience replay: Buffer (size = 10^6)
  • Game Environment
    • State dimension: (25,)
      • Agent: jumps, xpos, ypos, touchingObst, gravityCurrent
      • 5 Entities: x, y, speed, angle
    • Action space: (3,)
      • Agent: left, right, jump
      • Generator: weapon_type, x, y, angle

References

About

The Chosen One trained with Multi-Agent Reinforcement Learning and Generative Progressive Autocurricula, won Best AI Hack at iNTUition 2019.

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