Renamed files, added random motion
parent
d6452f5ed8
commit
fd02c65b41
GPG Key ID:
AD62BB059C2AAEE4
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@ -0,0 +1,230 @@
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import subprocess
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import time
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import threading
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import math
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class CelesteError(Exception):
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pass
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class Celeste:
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action_space = [
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"left", # move left
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"right", # move right
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"jump", # jump
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"dash-u", # dash up
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"dash-r", # dash right
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"dash-l", # dash left
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"dash-ru", # dash right-up
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"dash-lu" # dash left-up
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]
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def __init__(self, on_get_state):
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self.on_get_state = on_get_state
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# Start pico-8
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self.process = subprocess.Popen(
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"bin/pico-8/linux/pico8",
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shell=True,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT
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)
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# Wait for window to open and get window id
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time.sleep(2)
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winid = subprocess.check_output([
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"xdotool",
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"search",
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"--class",
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"pico8"
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]).decode("utf-8").strip().split("\n")
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if len(winid) != 1:
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raise Exception("Could not find unique PICO-8 window id")
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self.winid = winid[0]
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# Load cartridge
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self.keystring("load hackcel.p8")
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self.keypress("Enter")
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self.keystring("run")
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self.keypress("Enter", post = 1000)
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# Initialize variables
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self.internal_status = {}
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self.dead = False
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# Score system
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self.frame_counter = 0
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self.next_point = 0
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self.dist = 0 # distance to next point
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self.target_points = [
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[ # Stage 1
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(28, 88), # Start pillar
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(60, 80), # Middle pillar
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(105, 64), # Right ledge
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(25, 40), # Left ledge
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(110, 16), # End ledge
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(110, -2), # Next stage
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]
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]
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def act(self, action):
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self.keyup("x")
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self.keyup("c")
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self.keyup("Left")
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self.keyup("Right")
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self.keyup("Down")
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self.keyup("Up")
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if action is None:
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return
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elif action == "left":
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self.keydown("Left")
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elif action == "right":
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self.keydown("Right")
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elif action == "jump":
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self.keydown("c")
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elif action == "dash-u":
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self.keydown("Up")
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self.keydown("x")
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elif action == "dash-r":
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self.keydown("Right")
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self.keydown("x")
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elif action == "dash-l":
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self.keydown("Left")
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self.keydown("x")
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elif action == "dash-ru":
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self.keydown("Up")
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self.keydown("Right")
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self.keydown("x")
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elif action == "dash-lu":
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self.keydown("Up")
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self.keydown("Left")
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self.keydown("x")
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@property
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def status(self):
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try:
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return {
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"stage": (
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[
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[0, 1, 2, 3, 4]
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]
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[int(self.internal_status["ry"])]
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[int(self.internal_status["rx"])]
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),
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"xpos": int(self.internal_status["px"]),
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"ypos": int(self.internal_status["py"]),
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"xvel": float(self.internal_status["vx"]),
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"yvel": float(self.internal_status["vy"]),
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"deaths": int(self.internal_status["dc"]),
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"dist": self.dist,
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"next_point": self.next_point,
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"frame_count": self.frame_counter
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}
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except KeyError:
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raise CelesteError("Not enough data to get status.")
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def keypress(self, key: str, *, post = 200):
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subprocess.run([
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"xdotool",
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"key",
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"--window", self.winid,
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key
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])
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time.sleep(post / 1000)
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def keydown(self, key: str):
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subprocess.run([
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"xdotool",
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"keydown",
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"--window", self.winid,
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key
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])
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def keyup(self, key: str):
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subprocess.run([
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"xdotool",
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"keyup",
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"--window", self.winid,
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key
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])
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def keystring(self, string, *, delay = 100, post = 200):
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subprocess.run([
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"xdotool",
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"type",
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"--window", self.winid,
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"--delay", str(delay),
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string
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])
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time.sleep(post / 1000)
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def reset(self):
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self.internal_status = {}
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self.next_point = 0
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self.frame_counter = 0
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self.keypress("Escape")
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self.keystring("run")
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self.keypress("Enter", post = 1000)
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self.dead = False
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def flush_reader(self):
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for k in iter(self.process.stdout.readline, ""):
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k = k.decode("utf-8")[:-1]
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if k == "!RESTART":
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break
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def update_loop(self):
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# Get state, call callback, wait for state
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# One line => one frame.
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for line in iter(self.process.stdout.readline, ""):
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l = line.decode("utf-8")[:-1].strip()
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# This should only occur at game start
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if l in ["!RESTART"]:
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continue
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self.frame_counter += 1
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# Parse status string
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for entry in l.split(";"):
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if entry == "":
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continue
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key, val = entry.split(":")
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self.internal_status[key] = val
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# Update checkpoints
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tx, ty = self.target_points[self.status["stage"]][self.next_point]
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x = self.status["xpos"]
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y = self.status["ypos"]
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dist = math.sqrt(
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(x-tx)*(x-tx) +
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(y-ty)*(y-ty)
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)
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if dist <= 4 and y == ty:
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self.next_point += 1
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# Recalculate distance to new point
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tx, ty = self.target_points[self.status["stage"]][self.next_point]
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dist = math.sqrt(
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(x-tx)*(x-tx) +
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(y-ty)*(y-ty)
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)
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self.dist = dist
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# Call step callback
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self.on_get_state(self)
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@ -1,213 +1,179 @@
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import subprocess
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import time
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import threading
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from collections import namedtuple
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from collections import deque
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import random
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import math
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class Celeste:
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import torch
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def __init__(self):
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# Start process
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self.process = subprocess.Popen(
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"bin/pico-8/linux/pico8",
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shell=True,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT
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# Glue layer
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from celeste import Celeste
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compute_device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu"
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)
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# Epsilon-greedy parameters
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#
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# Original docs:
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# EPS_START is the starting value of epsilon
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# EPS_END is the final value of epsilon
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# EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay
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EPS_START = 0.9
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EPS_END = 0.05
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EPS_DECAY = 1000
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# Outline our network
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class DQN(torch.nn.Module):
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def __init__(self, n_observations: int, n_actions: int):
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super(DQN, self).__init__()
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self.layer1 = torch.nn.Linear(n_observations, 128)
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self.layer2 = torch.nn.Linear(128, 128)
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self.layer3 = torch.nn.Linear(128, n_actions)
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# Can be called with one input, or with a batch.
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#
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# Returns tensor(
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# [ Q(s, left), Q(s, right) ], ...
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# )
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#
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# Recall that Q(s, a) is the (expected) return of taking
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# action `a` at state `s`
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def forward(self, x):
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x = torch.nn.functional.relu(self.layer1(x))
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x = torch.nn.functional.relu(self.layer2(x))
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return self.layer3(x)
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# Celeste env properties
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n_observations = 4
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n_actions = len(Celeste.action_space)
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policy_net = DQN(
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n_observations,
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n_actions
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).to(compute_device)
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def select_action(state, steps_done):
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"""
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Select an action using an epsilon-greedy policy.
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Sometimes use our model, sometimes sample one uniformly.
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P(random action) starts at EPS_START and decays to EPS_END.
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Decay rate is controlled by EPS_DECAY.
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"""
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# Random number 0 <= x < 1
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sample = random.random()
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# Calculate random step threshhold
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eps_threshold = (
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EPS_END + (EPS_START - EPS_END) *
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math.exp(
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-1.0 * steps_done /
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EPS_DECAY
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)
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)
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# Wait for window to open and get window id
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time.sleep(2)
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winid = subprocess.check_output([
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"xdotool",
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"search",
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"--class",
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"pico8"
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]).decode("utf-8").strip().split("\n")
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if len(winid) != 1:
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raise Exception("Could not find unique PICO-8 window id")
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self.winid = winid[0]
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if sample > eps_threshold:
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with torch.no_grad():
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# t.max(1) will return the largest column value of each row.
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# second column on max result is index of where max element was
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# found, so we pick action with the larger expected reward.
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return policy_net(state).max(1)[1].view(1, 1).item()
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# Load cartridge
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self.keystring("load hackcel.p8")
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self.keypress("Enter")
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self.keystring("run")
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self.keypress("Enter", post = 1000)
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# Initialize variables
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self.internal_status = {}
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self.dead = False
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# -1: left
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# 0: not moving
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# 1: moving right
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self.moving = 0
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# Start state update thread
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self.update_thread = threading.Thread(target = self._update_loop)
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self.update_thread.start()
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def act(self, action):
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self.keyup("x")
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self.keyup("c")
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self.keyup("Down")
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self.keyup("Up")
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if self.moving != -1:
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self.keyup("Left")
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if self.moving != 1:
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self.keyup("Right")
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if action is None:
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self.moving = 0
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self.keyup("Left")
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self.keyup("Right")
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elif action == "left":
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if self.moving != -1:
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self.keydown("Left")
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self.moving = -1
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elif action == "right":
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if self.moving != 1:
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self.keydown("Right")
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self.moving = 1
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else:
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return random.randint( 0, n_actions-1 )
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@property
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def status(self):
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return {
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"stage": (
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[
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[0, 1, 2, 3, 4]
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]
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[int(self.internal_status["ry"])]
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[int(self.internal_status["rx"])]
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),
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"xpos": int(self.internal_status["px"]),
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"ypos": int(self.internal_status["py"]),
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"xvel": float(self.internal_status["vx"]),
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"yvel": float(self.internal_status["vy"])
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}
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last_state = None
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Transition = namedtuple(
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"Transition",
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(
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"state",
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"action",
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"next_state",
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"reward"
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)
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)
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# Possible actions
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@property
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def action_space(self):
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return [
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"left", # move left
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"rght", # move right
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"jump", # jump
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"dshn", # dash north
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"dshe", # dash east
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"dshw", # dash west
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"dsne", # dash north-east
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"dsnw" # dash north-west
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def on_state(celeste):
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global last_state
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s = celeste.status
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if last_state is None:
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last_state = s
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return
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s_next = s["next_point"]
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s_dist = s["dist"]
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l_next = last_state["next_point"]
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l_dist = last_state["dist"]
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if l_next == s_next:
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reward = l_dist - s_dist
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else:
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reward = 10
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dead = s["deaths"] != 0
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frame_count = s["frame_count"]
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# Values at this point
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# reward: reward for last action
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# dead: true if game over
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state_number_map = [
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"xpos",
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"ypos",
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"xvel",
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"yvel"
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]
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tf_state = torch.tensor(
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[s[x] for x in state_number_map],
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dtype = torch.float32,
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device = compute_device
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).unsqueeze(0)
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def keypress(self, key: str, *, post = 200):
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subprocess.run([
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"xdotool",
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"key",
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"--window", self.winid,
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key
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])
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time.sleep(post / 1000)
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def keydown(self, key: str):
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subprocess.run([
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"xdotool",
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"keydown",
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"--window", self.winid,
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key
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])
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def keyup(self, key: str):
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subprocess.run([
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"xdotool",
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"keyup",
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"--window", self.winid,
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key
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])
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def keystring(self, string, *, delay = 100, post = 200):
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subprocess.run([
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"xdotool",
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"type",
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"--window", self.winid,
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"--delay", str(delay),
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string
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])
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time.sleep(post / 1000)
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def reset(self):
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self.internal_status = {}
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if not self.dead:
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self.keypress("Escape")
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self.keystring("run")
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self.keypress("Enter", post = 1000)
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self.dead = False
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def _update_loop(self):
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# Poll process for new output until finished
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for line in iter(self.process.stdout.readline, ""):
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l = line.decode("utf-8")[:-1]
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if l in ["!RESTART"]:
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continue
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for entry in l.split(";"):
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key, val = entry.split(":")
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self.internal_status[key] = val
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# Exit game on death
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if "dc" in self.internal_status and self.internal_status["dc"] != "0":
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self.keypress("Escape")
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self.dead = True
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# Flush stream reader
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for k in iter(self.process.stdout.readline, ""):
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k = k.decode("utf-8")[:-1]
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if k == "!RESTART":
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break
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tf_last = torch.tensor(
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[last_state[x] for x in state_number_map],
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dtype = torch.float32,
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device = compute_device
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).unsqueeze(0)
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# Stage 1:
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next_point = 0
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target_points = [
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(28, 88), # Start pillar
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(60, 80), # Middle pillar
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(105, 64), # Right ledge
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(25, 40), # Left ledge
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(110, 16), # End ledge
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(110, -2), # Next stage
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]
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# += 5
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|
||||
c = Celeste()
|
||||
while True:
|
||||
if c.dead:
|
||||
print("\n\nDead, resetting...")
|
||||
c.reset()
|
||||
|
||||
|
||||
tx, ty = target_points[next_point]
|
||||
x = c.status["xpos"]
|
||||
y = c.status["ypos"]
|
||||
|
||||
dist = math.sqrt(
|
||||
(x-tx)*(x-tx) +
|
||||
(y-ty)*(y-ty)
|
||||
action = select_action(
|
||||
tf_state,
|
||||
frame_count
|
||||
)
|
||||
|
||||
if dist <= 4 and y == ty:
|
||||
next_point += 1
|
||||
# Turn number into action string
|
||||
action = Celeste.action_space[action]
|
||||
|
||||
print(f"Target point: {next_point:02}, Dist: {dist:0.3}")
|
||||
celeste.act(action)
|
||||
|
||||
#print()
|
||||
#print(c.status)
|
||||
|
||||
|
||||
# Update previous state
|
||||
last_state = s
|
||||
|
||||
|
||||
|
||||
c = Celeste(
|
||||
on_state
|
||||
)
|
||||
|
||||
c.update_loop()
|
||||
|
|
|
|||
Reference in New Issue