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RL algorithms for solving supermario game in gym environment

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mario_rl

Environment

Reward Function

Reward Function

The reward function assumes the objective of the game is to move as far right as possible (increase the agent's x value), as fast as possible, without dying. To model this game, three separate variables compose the reward:

  1. v: the difference in agent x values between states
    • in this case this is instantaneous velocity for the given step
    • v = x1 - x0
      • x0 is the x position before the step
      • x1 is the x position after the step
    • moving right ⇔ v > 0
    • moving left ⇔ v < 0
    • not moving ⇔ v = 0
  2. c: the difference in the game clock between frames
    • the penalty prevents the agent from standing still
    • c = c0 - c1
      • c0 is the clock reading before the step
      • c1 is the clock reading after the step
    • no clock tick ⇔ c = 0
    • clock tick ⇔ c < 0
  3. d: a death penalty that penalizes the agent for dying in a state
    • this penalty encourages the agent to avoid death
    • alive ⇔ d = 0
    • dead ⇔ d = -15

r = v + c + d

RL Learning

DQN

Vaillna DQN (no double, no deuling, no PER)

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RL algorithms for solving supermario game in gym environment

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