70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
from agent.Base_Agent import Base_Agent
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from behaviors.custom.Stop.Env import Env
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from math_ops.Math_Ops import Math_Ops as M
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from math_ops.Neural_Network import run_mlp
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import numpy as np
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import pickle
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class Stop:
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def __init__(self, base_agent: Base_Agent) -> None:
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self.last_executed = 0
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self.behavior = base_agent.behavior
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self.path_manager = base_agent.path_manager
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self.world = base_agent.world
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self.description = "RL dribble"
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self.auto_head = True
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self.env = Env(base_agent, 0.9 if self.world.robot.type == 3 else 1.2)
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with open(M.get_active_directory([
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"/behaviors/custom/Stop/best_model.zip.pkl",
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"/behaviors/custom/Stop/best_model.zip.pkl",
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"/behaviors/custom/Stop/best_model.zip.pkl",
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"/behaviors/custom/Stop/best_model.zip.pkl",
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"/behaviors/custom/Stop/best_model.zip.pkl"
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][self.world.robot.type]), 'rb') as f:
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self.model = pickle.load(f)
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def execute(self, reset):
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'''
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Parameters
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----------
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target_2d : array_like
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2D target in absolute or relative coordinates (use is_target_absolute to specify)
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is_target_absolute : bool
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True if target_2d is in absolute coordinates, False if relative to robot's torso
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orientation : float
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absolute or relative orientation of torso, in degrees
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set to None to go towards the target (is_orientation_absolute is ignored)
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is_orientation_absolute : bool
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True if orientation is relative to the field, False if relative to the robot's torso
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distance : float
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distance to final target [0,0.5] (influences walk speed when approaching the final target)
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set to None to consider target_2d the final target
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'''
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r = self.world.robot
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# ------------------------ 0. Override reset (since some behaviors use this as a sub-behavior)
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if reset and self.world.time_local_ms - self.last_executed == 20:
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reset = False
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self.last_executed = self.world.time_local_ms
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# ------------------------ 1. Define walk parameters
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self.env.walk_rel_target = (0, 0)
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self.env.walk_distance = 0 # MAX_LINEAR_DIST = 0.5
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self.env.walk_rel_orientation = 0
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# ------------------------ 2. Execute behavior
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obs = self.env.observe(reset)
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action = run_mlp(obs, self.model)
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if self.env.execute(action):
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return True
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return False
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def is_ready(self):
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''' Returns True if this behavior is ready to start/continue under current game/robot conditions '''
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return True
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