+block2d/gym/stable_baseline thing
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6
Python/ai/block_game_gym/Makefile
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6
Python/ai/block_game_gym/Makefile
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clean:
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-rm -r logs/PPO_* logs/A2C_*
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-rm frame_*.png
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gif:
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convert -delay 10 -loop 0 *.png output.gif
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132
Python/ai/block_game_gym/block2d.py
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132
Python/ai/block_game_gym/block2d.py
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from pyray import *
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#class Block2d:
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# def __init__(
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# self,
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# starting_position=Vector2(100, 100),
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# goal_position=Vector2(0, 0)
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# ):
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# self.position = starting_position
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# self.goal_position = goal_position
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# self.direction = Vector2(0, 0)
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# self.max_speed = 1200
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# self.speed = Vector2(0, 0)
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# self.last_update = 0 #= get_time()
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# self.last_click = Vector2(-10, -10)
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#
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# def control(self, click: Vector2):
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# def signum(x): return int(x > 0) - int(x < 0)
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# def accelerate(v: float, extra: float) -> float:
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# v = abs(v + extra) if abs(v + extra) < self.max_speed else self.max_speed
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# return v
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# self.last_click = click
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# self.direction = Vector2(self.position.x - click.x, self.position.y - click.y)
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# self.speed.x = accelerate(self.speed.x, self.direction.x)
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# self.speed.y = accelerate(self.speed.y, self.direction.y)
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# self.direction = Vector2(
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# signum(self.direction.x),
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# signum(self.direction.y),
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# )
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#
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# def update(self):
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# def decelerate(v : float) -> float:
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# if v - 1.1 > 0: v -= 1.1
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# else: v = 0
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# return v
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# update_time = self.last_update + 0.01 #get_time()
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# delta_time = update_time - self.last_update
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# self.position.x -= self.speed.x * delta_time * self.direction.x
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# self.position.y -= self.speed.y * delta_time * self.direction.y
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#
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# self.speed.x = decelerate(self.speed.x)
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# self.speed.y = decelerate(self.speed.y)
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#
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# self.last_update = update_time
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#
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# def is_win_condition(self):
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# return (
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# self.speed.x < 0 and
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# self.speed.y < 0 and
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# abs(self.position.x - self.goal_position.x) < 50 and
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# abs(self.position.y - self.goal_position.y) < 50
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# )
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#
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#
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# def display(self):
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# def draw_hitmark(x : int, y : int) -> None:
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# size = 10
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# draw_line(x - size, y - size, x + size, y + size, GREEN)
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# draw_line(x + size, y - size, x - size, y + size, GREEN)
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# draw_rectangle_lines(x - size, y - size, size*2, size*2, GREEN)
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# # Blocks
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# draw_rectangle(int(self.goal_position.x), int(self.goal_position.y), 30, 30, BLUE)
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# draw_rectangle(int(self.position.x), int(self.position.y), 30, 30, RED)
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#
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# # Hud
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# draw_text(f"X: {self.position.x:.2f}", 10, 10, 20, DARKGRAY)
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# draw_text(f"Y: {self.position.y:.2f}", 10, 40, 20, DARKGRAY)
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# draw_text(f"Speed X: {self.speed.x:.2f}", 10, 70, 20, DARKGRAY)
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# draw_text(f"Speed Y: {self.speed.y:.2f}", 10, 100, 20, DARKGRAY)
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#
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# # Player action
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# draw_hitmark(int(self.last_click.x), int(self.last_click.y))
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#
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# # Win
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# if self.is_win_condition(): draw_text(f"Victory!", 200, 200, 40, DARKGRAY)
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# NOTE:
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# same as above with no physics
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class Block2d:
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def __init__(
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self,
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starting_position=Vector2(100, 100),
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goal_position=Vector2(0, 0)
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):
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self.position = starting_position
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self.goal_position = goal_position
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self.last_click = Vector2(-10, -10)
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def control(self, click: Vector2):
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def signum(x): return int(x > 0) - int(x < 0)
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def accelerate(v: float, extra: float) -> float:
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v = abs(v + extra) if abs(v + extra) < self.max_speed else self.max_speed
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return v
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self.last_click = click
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self.direction = Vector2(self.position.x - click.x, self.position.y - click.y)
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#self.speed.x = accelerate(self.speed.x, self.direction.x)
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#self.speed.y = accelerate(self.speed.y, self.direction.y)
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#self.direction = Vector2(
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# signum(self.direction.x),
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# signum(self.direction.y),
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#)
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self.position.x -= self.direction.x
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self.position.y -= self.direction.y
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def update(self):
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pass
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def is_win_condition(self):
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return (
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abs(self.position.x - self.goal_position.x) < 50 and
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abs(self.position.y - self.goal_position.y) < 50
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)
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def display(self):
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def draw_hitmark(x : int, y : int) -> None:
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size = 10
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draw_line(x - size, y - size, x + size, y + size, GREEN)
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draw_line(x + size, y - size, x - size, y + size, GREEN)
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draw_rectangle_lines(x - size, y - size, size*2, size*2, GREEN)
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# Blocks
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draw_rectangle(int(self.goal_position.x), int(self.goal_position.y), 30, 30, BLUE)
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draw_rectangle(int(self.position.x), int(self.position.y), 30, 30, RED)
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# Hud
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draw_text(f"X: {self.position.x:.2f}", 10, 10, 20, DARKGRAY)
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draw_text(f"Y: {self.position.y:.2f}", 10, 40, 20, DARKGRAY)
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# Player action
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draw_hitmark(int(self.last_click.x), int(self.last_click.y))
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# Win
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if self.is_win_condition(): draw_text(f"Victory!", 200, 200, 40, DARKGRAY)
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30
Python/ai/block_game_gym/game.py
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30
Python/ai/block_game_gym/game.py
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from pyray import *
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from block2d import Block2d
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screen_width = 800
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screen_height = 600
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def main():
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init_window(screen_width, screen_height, "Physics Simulation")
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set_target_fps(60)
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content = Block2d(
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Vector2(screen_width / 2, screen_height / 2),
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Vector2(screen_width / 3, screen_height / 3),
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)
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while not window_should_close():
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if is_mouse_button_pressed(MOUSE_LEFT_BUTTON):
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content.control(get_mouse_position())
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content.update()
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begin_drawing()
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clear_background(RAYWHITE)
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content.display()
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end_drawing()
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close_window()
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if __name__ == "__main__":
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main()
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252
Python/ai/block_game_gym/main.py
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252
Python/ai/block_game_gym/main.py
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from os import getcwd, chdir
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from time import sleep
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from random import randint
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from block2d import Block2d
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from pyray import *
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import numpy as np
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import gymnasium as gym
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from gymnasium import spaces
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from gymnasium.utils.env_checker import check_env
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from stable_baselines3 import *
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screen_width = 800
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screen_height = 600
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class BlockGameEnvironment(gym.Env):
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metadata = {
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"render_modes": ["human"],
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"render_fps": 20,
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}
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def __init__(self, render_mode=None):
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#super().__init__()
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# Raylib
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init_window(screen_width, screen_height, "Physics Simulation")
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self.frame_counter = 0
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self.content = Block2d()
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# Action space
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# with normalization
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self.action_space = spaces.Box(
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low=np.array([-1, -1, -1]),
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high=np.array([1, 1, 1]),
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dtype=np.float32
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)
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# without normalization
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#self.action_space = spaces.Box(
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# low=np.array([0, 0, 0]),
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# high=np.array([screen_width, screen_width, screen_height]),
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# dtype=np.float32
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#)
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# Observation space
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self.observation_space = spaces.Box(
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low=np.array([0] * 6),
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high=np.array([
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screen_width,
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screen_height,
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screen_width,
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screen_height,
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#self.content.max_speed,
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#self.content.max_speed,
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1,
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1,
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]),
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dtype=np.float64
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)
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def reset(self, seed=None, options=None):
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super().reset(seed=seed)
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# Reconstruct the game
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del self.content
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self.box_start = (randint(0, screen_width), randint(0, screen_height))
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self.goal_position = (randint(0, screen_width), randint(0, screen_height))
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self.content = Block2d(
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Vector2(*self.box_start),
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Vector2(*self.goal_position),
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)
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obs = np.array([
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self.content.position.x,
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self.content.position.y,
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self.content.goal_position.x,
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self.content.goal_position.y,
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#self.content.speed.x,
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#self.content.speed.y,
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1,
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1,
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])
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return obs, {}
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def step(self, action):
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def calc_reward(is_done : bool) -> float:
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# We have many strategies to reward the model,
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# one worse than the other.
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def keep_moving_large_proximity_big_earner():
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if is_done: return 100
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diff = (
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abs(self.content.goal_position.x - action[0]),
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abs(self.content.goal_position.y - action[1]),
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)
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if diff[0] < 100 and diff[1] < 100:
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return 1 / (diff[0] + diff[1])
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if self.content.speed.x == 0 and self.content.speed.y == 0:
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return -0.001
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return -0.0001
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def precise_proximity_punisher():
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r = 0
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diffs = (
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self.content.goal_position.x - self.content.position.x,
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self.content.goal_position.y - self.content.position.y
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)
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diffs = (abs(diffs[0]), abs(diffs[1]))
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speed_sum = self.content.speed.x + self.content.speed.y
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r -= (diffs[0] + diffs[1]) / 1000
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r -= speed_sum / 1200
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return r
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def take_it_slow_large_proximity_big_earner():
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if is_done: return 100
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elif (
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self.content.speed.x < 100 and
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self.content.speed.y < 100 and
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abs(self.content.position.x - self.content.goal_position.x) < 50 and
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abs(self.content.position.y - self.content.goal_position.y) < 50
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): return 1
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else: return -0.0001
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def just_move_near_it_im_begging_you():
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r = 0
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diffs = (
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self.content.goal_position.x - self.content.position.x,
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self.content.goal_position.y - self.content.position.y
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)
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dist = np.sqrt(diffs[0]**2 + diffs[1]**2)
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r = dist / 10_000
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return -r
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def just_move_near_it_im_begging_you_normalized():
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if is_done: return 10
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r = 0
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max_dist = np.sqrt(screen_width**2 + screen_height**2)
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diffs = (
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self.content.goal_position.x - self.content.position.x,
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self.content.goal_position.y - self.content.position.y
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)
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dist = np.sqrt(diffs[0]**2 + diffs[1]**2)
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r = dist / max_dist
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return -r
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return just_move_near_it_im_begging_you_normalized()
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# normalization
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#should_move = action[0] > 0.8
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click = Vector2(action[1], action[2])
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click.x = ((click.x + 1) * screen_width) / 2
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click.y = ((click.y + 1) * screen_height) / 2
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should_move = True
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#click = Vector2(action[1], action[2])
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if should_move:
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self.content.control(click)
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self.content.update()
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obs = np.array([
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self.content.position.x,
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self.content.position.y,
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self.content.goal_position.x,
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self.content.goal_position.y,
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#self.content.speed.x,
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#self.content.speed.y,
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1,
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1,
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])
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is_done = self.content.is_win_condition()
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reward = calc_reward(is_done)
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# printf debugging
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#print(f"obs: {obs}")
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#print(f"act: {action}")
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#print(f"rew: {reward}")
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return obs, reward, is_done, False, {}
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def render(self, mode="human"):
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begin_drawing()
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clear_background(RAYWHITE)
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self.content.display()
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end_drawing()
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if self.frame_counter < 400: take_screenshot(f"frame_{self.frame_counter:05d}.png")
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else: sleep(0.01)
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self.frame_counter += 1
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def close(self):
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close_window()
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# Env init
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gym.envs.registration.register(
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id="BlockGameEnvironment-v0",
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entry_point=__name__+":BlockGameEnvironment",
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)
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env = gym.make("BlockGameEnvironment-v0", render_mode="human")
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# very useful check, however it clones the environment,
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# which is bad in our case because raylib uses global data
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#check_env(env.unwrapped)
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# Model init
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def get_mymodel(name : str, do_train : bool):
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model_name = "custom_model_-_sorta_works"
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model = None
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match name:
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case "myPPO":
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model = PPO(
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"MlpPolicy",
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env,
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learning_rate=0.001,
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ent_coef=0.1,
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batch_size=2, # NOTE: this is why we dont marge the two cases
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verbose=1,
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tensorboard_log="logs/",
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)
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case "myA2C":
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model = A2C( # really, really fast to train
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"MlpPolicy",
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env,
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verbose=1,
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tensorboard_log="logs/",
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)
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if do_train:
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for i in range(5000): model.learn(100)
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model.save(model_name)
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else:
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match name:
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case "myPPO":
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model = PPO.load(model_name, env=env)
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case "myA2C":
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model = A2C.load(model_name, env=env)
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return model
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model = get_mymodel(
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# "myPPO",
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"myA2C",
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# True,
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False,
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)
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# Show what the model learned
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while True:
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obs, _ = env.reset()
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for _ in range(1000):
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action, _ = model.predict(obs)
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obs, _, done, _, _ = env.step(action)
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print(action)
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env.render()
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if done: break
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6
Python/ai/block_game_gym/requirements.txt
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6
Python/ai/block_game_gym/requirements.txt
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matplotlib
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numpy
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gymnasium
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raylib
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stable_baselines3
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tensorboard
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