import numpy as np
from deepbots.supervisor.controllers.supervisor_emitter_receiver import SupervisorCSV
from PPOAgent import PPOAgent, Transition
from utilities import normalizeToRange
from ArmUtil import ToArmCoord, PSFunc
from CalObs import get_delta_r_and_theta
import random as rand
# from ikpy.chain import Chain
# from ikpy.link import OriginLink, URDFLink
class PandaSupervisor(SupervisorCSV):
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import matplotlib.pyplot as plt
number = input("1 for constrain score, 2 for len score, 3 for orientation score, 4 for total score")
if number == 1 :
fp = open("Episode-constrain-score.txt", "r")
print("now showing Episode-constrain-score")
elif number == 2 :
fp = open("Episode-len-score.txt", "r")
print("now showing Episode-len-score")
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Deepbots官方介紹
https://github.com/aidudezzz/deepbots
Deepbots is a simple framework which is used as "middleware" between the free and open-source Cyberbotics' Webots robot simulator and Reinforcement Learning algorithms. When it comes to Reinforcement Learning the OpenAI gym environment has been established as the most used interface between the actual application and the RL algorithm. Deepbots is a framework which follows the OpenAI gym environment interface logic in order to be used by Webots applications.
deepbots套件解釋
supervisor.py
從agent得到選擇的action
selectedAction, actionProb = agent.work(observation, type_="selectAction")
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tags: dopamine
Pypi: https://pypi.org/project/dopamine-rl/
使用須知
27/07/2020: Dopamine now runs on TensorFlow 2. However, Dopamine is still written as TensorFlow 1.X code. This means your project may need to explicity disable TensorFlow 2 behaviours with:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
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看 paper 並製作 ppt
deadline: 8/14(本周五)
Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection
負責人:蘇晏鋒
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
負責人:石詠太
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
負責人:陳楚翔
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs
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Slides
Abstract & Introduction
For efficient embedded deep learning system
Facing resource and power constraints
Need co-design between DNN algorithm, memory hierarchy, and dataflow
A lot of research has been done towards developing specialized hardware accelerators, but ad-hoc and local-optimal resulting from limited design space exploration
DSE: Design Space Exploration
3 main design spaces:
kai2679 changed 5 years agoView mode Like Bookmark
deepbots套件解釋
reward機制
# compute reward here
## do not exceed the limit value
if -2.897<newObservation[0] and newObservation[0]<2.897 and\
-1.763<newObservation[1] and newObservation[1]<1.763 and\
-2.8973<newObservation[2] and newObservation[2]<2.8973 and\
-3.072<newObservation[3] and newObservation[3]<-0.0698 and\
-2.8973<newObservation[4] and newObservation[4]<2.8973 and\
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