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44 lines
1.8 KiB
Python
44 lines
1.8 KiB
Python
from agent.Base_Agent import Base_Agent
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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 pickle, numpy as np
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class Fall():
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def __init__(self, base_agent : Base_Agent) -> None:
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self.world = base_agent.world
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self.description = "Fall example"
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self.auto_head = False
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with open(M.get_active_directory("/behaviors/custom/Fall/fall.pkl"), 'rb') as f:
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self.model = pickle.load(f)
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self.action_size = len(self.model[-1][0]) # extracted from size of Neural Network's last layer bias
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self.obs = np.zeros(self.action_size+1, np.float32)
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self.controllable_joints = min(self.world.robot.no_of_joints, self.action_size) # compatibility between different robot types
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def observe(self):
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r = self.world.robot
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for i in range(self.action_size):
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self.obs[i] = r.joints_position[i] / 100 # naive scale normalization
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self.obs[self.action_size] = r.cheat_abs_pos[2] # head.z (alternative: r.loc_head_z)
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def execute(self,reset) -> bool:
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self.observe()
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action = run_mlp(self.obs, self.model)
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self.world.robot.set_joints_target_position_direct( # commit actions:
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slice(self.controllable_joints), # act on trained joints
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action*10, # scale actions up to motivate early exploration
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harmonize=False # there is no point in harmonizing actions if the targets change at every step
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)
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return self.world.robot.loc_head_z < 0.15 # finished when head height < 0.15 m
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def is_ready(self) -> any:
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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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