INNER CODE UNIT · Python

prob

rlcode/reinforcement-learning · 1-grid-world/2-value_iteration.py:76

            prob = 1.0 / len(actions)
            for a in actions:
                policy[x][y][a] = prob
        display.show_arrows(policy)

    def on_move():
        display.move_along_policy(value_iteration.get_action)

    def on_clear():
        value_iteration.__init__(env)
        display.clear()
        display.agent_pos = [0, 0]
        display.clicks.clear()

    # Workflow: Calculate x several (until V stops changing) -> Print Policy -> Move.
    # Print Policy unlocks after Calculate; Move unlocks after Print Policy.
    display.buttons = [
        ("Calculate",    on_calculate),

View source record →