The Chosen One - Multi-Agent Reinforcement Learning with Autocurricula

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:

Agent learnt to dodge Gun bullets:

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

• DDQN - Deep Reinforcement Learning with Double Q-learning
• ChainerRL - Deep Reinforcement Learning Library
• PyGame - Python Game Library