Q-learning is a fundamental algorithm that acts as the springboard for the deep reinforcement learning algorithms used to beat humans at Go and DOTA. We evaluate our approach with a navigation task, where a quadcopter drone flies between landmarks following natural … Reinforcement Learning: Quadcopter Control Automation (the code of this project is prohibited from being shared due to confidentiality) Recurrent Neural Network, Embeddings and Word2Vec, Sentiment Analysis: TV Script Generation. Introduction. Learn more. Convolutional Neural Network, Autoencoders: Dog Breed Identification Generative Deep Learning using recurrent neural network to create new TV scripts. To use this simulator for reinforcement learning we developed a The goal of this project is to train a quadcopter to fly with a deep reinforcement learning algorithm, specifically it is trained how to take-off. Using reinforcement learning, you can train a network to directly map state to actuator commands. Close. class: center, middle # Lecture 1: ### Introduction to Deep Learning ### ... and your setup! human interaction. I am a PhD student at MIT, on leave until Fall 2021.I am an avid proponent of reform in machine learning, which allows me to spend time on teaching, mentoring, and alternative proposals for research distribution.I am lucky to be a GAAP mentor and a Machine Learning mentor, both of which are initiatives trying to level the playing field when it comes to machine learning academia. We’ve witnessed the advent of a new era for robotics recently due to advances in control methods and reinforcement learning algorithms, where unmanned aerial vehicles (UAV) have demonstrated promising potential for both civil and commercial applications. The idea behind this project is to teach a simulated quadcopter how to perform some activities. if you don't use anaconda, install those packages Udacity Reinforcement Learning Project: Train a Quadcopter How to Fly. I also helped design and build USC's Crazyswarm 49-quadcopter research facility. Mirroring without Overimitation Training a Quadcopter to Autonomously Learn to Track AoG. These algorithms achieve very good performance but require a lot of training data. Reinforcement Learning Quadcopter Environment. Figure 1: Our meta-reinforcement learning method controlling a quadcopter transporting a suspended payload. In Proceedings of the 2014 AAAI Spring Symposium Series. YouTube Companion Video; Q-learning is a model-free reinforcement learning technique. Improved and generalized code structure. download the GitHub extension for Visual Studio. GitHub. With reinforcement learning, a common network can be trained to directly map state to actuator command making any predefined control structure obsolete for training. A library for reinforcement learning in TensorFlow. This task is challenging since each payload induces different system dynamics, which requires the quadcopter controller to adapt online. Bhairav Mehta. Practical walkthroughs on machine learning, data exploration and finding insight. Quadcopter Reinforcement Machine Learning- Machine learning proof of concept to teach a quadcopter to take off and land safely. Reinforcement Learning. Q-learning - Wikipedia. In summer of 2019, I visited Google NYC as a research intern. Use Git or checkout with SVN using the web URL. Mirroring without Overimitation Bilevel Optimization. We also introduce a new learning algorithm that we used to train a quadrotor. TF-Agents makes designing, implementing and testing new RL algorithms easier. ... Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning). 2014. Daniel Dewey. Google Scholar; Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, et al. The Papers • Learning to Map Natural Language Instructions to Physical Quadcopter Control Using Simulated Flight Valts Blukis, Yannick Terme, Eyvind Niklasson, … MetaStyle: Trading Off Speed, Flexibility, and Quality in Neural Style Transfer Neural Style Transfer. agents/agent.py: This file defines the the DDPG algorithm. We demonstrate that, using zero-bias, zero-variance samples, we can stably learn a high-performance policy for a quadrotor. If nothing happens, download Xcode and try again. JUNE, 2017 1 Control of a Quadrotor with Reinforcement Learning Jemin Hwangbo1, Inkyu Sa2, Roland Siegwart2 and Marco Hutter1 Abstract—In this paper, we present a method to control a Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. NeuralTalk2. Week 7 - Model-Based reinforcement learning - MB-MF The algorithms studied up to now are model-free, meaning that they only choose the better action given a state. Reinforcement learning and the reward engineering principle. This a summary of our IJCAI 2018 paper in training a quadcopter to learn to track.. 1. NeurIPS 2018 (Spotlight presentation, ~4% of submitted papers).Talks “Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models.” If nothing happens, download GitHub Desktop and try again. This a summary of our IJCAI 2018 paper in training a quadcopter to learn to track.. 1. In Proceedings of the 2014 AAAI Spring Symposium Series. Flying a Quadcopter . GitHub. This video shows the results of using Proximal Policy Optimiation (PPO) Deep Reinforcement Learning agent to learn a non-trivial quadcopter-landing task. Reinforcement learning and the reward engineering principle. Generative Deep Learning using RNN. 2966 . GitHub Gist: instantly share code, notes, and snippets. Shixiang Gu*, Ethan Holly*, Timothy Lillicrap, Sergey Levine. Actor Learning Rate 1e 4 Critic Learning Rate 1e 3 Target network tracking parameter, ˝ 0.125 Discount Factor, 0.98 # episodes 2500 3.5 Simulation Environment The quadcopter is simulated using the Gazebo simulation engine, with the hector_gazebo[9] ROS package modified to our needs. The full report can be found in the Quadcopter_Project.ipynb notebook. In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter … Deep Reinforcement Learning has recently gained a lot of traction in the machine learning community due to the significant amount of progress that has been made in the past few years. on reinforcement learning without any additional PID compo-nents. Inverted Pendulum on a Quadcopter: A Reinforcement Learning Approach Physical Sciences Alexandre El Assad aelassad@stanford.edu Elise Fournier-Bidoz efb@stanford.edu Pierre Lachevre lpierre@stanford.edu Javier Sagastuy jvrsgsty@stanford.edu December 15th, 2017 CS229 - Final Report 1 … Quadcopter Project. Specifically, Q-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP). Applied Deep Q learning to navigation of autonomous quadcopters. Introduction. Contribute to yoavalon/QuadcopterReinforcementLearning development by creating an account on GitHub. Quadcopter_Project.ipynb: This Jupyter Notebook provides part of the code for training the quadcopter and a summary of the implementation and results. INTRODUCTION In recent years, Quadcopters have been extensively used for civilian task like object tracking, disaster rescue, wildlife protection and asset localization. ∙ 70 ∙ share . The depthmap from a depthcam was taken as input to generate movement commands for a quadcopter. GitHub. Finally, an investigation of control using reinforcement learning is conducted. Training a drone using deep reinforcement learning w openai gym pksvvdeep reinforcement learning quadcopter. Along with implementation of the reinforcemnt learning algorithm, this project involved building a controller on top of the MAVROS framework and simulating using PX4 and PX4 SITL. PREPRINT VERSION. Abnormal Pedestrians Behaviour Detection August 2016 GitHub. QuadCopter-RL. physics_sim.py: This file introduces a physical simulator for the motion of the quadcopter. Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads Suneel Belkhale y, Rachel Li , Gregory Kahn , Rowan McAllister , Roberto Calandraz, Sergey Leviney yBerkeley AI Research, zFacebook AI Research (a) (b) (c) (d) (e) Fig. 7214 . In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. Waypoint-based trajectory control of a quadcopter is performed and appended to the MATLAB toolbox. Using DDPG agent to allow a quadcopter to learn how to takeoff and land. 2017. ... 2928 . Teaching a QuadCopter to TakeOff and Land using Reinforcement Learning. quadcopter control using reinforcement learning. GitHub, GitLab or BitBucket ... Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors. Reinforcement Learning; Edit on GitHub; Reinforcement Learning in AirSim# We below describe how we can implement DQN in AirSim using an OpenAI gym wrapper around AirSim API, and using stable baselines implementations of standard RL algorithms. I. Programmable Engine for Drone Reinforcement Learning Applications View on GitHub Programmable Engine for Drone Reinforcement Learning (RL) Applications (PEDRA-2.0) Updates in version 2.0: Support of multi-drone environments. Neural Network that automatically adds color to black and white images. the quadcopter (comparatively simple UAV design without thrust vectoring). The new algorithm is a deterministic on-policy method which is not common in reinforcement learning. It’s even possible to completely control a quadcopter using a neural network trained in simulation! In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques. Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning. Have you heard about the amazing results achieved by Deepmind with AlphaGo Zero and by OpenAI in Dota 2? Now it is the time to get our hands dirty and practice how to implement the models in the wild. If nothing happens, download the GitHub extension for Visual Studio and try again. With the encouragement from the reviewers of my last project — a Reinforcement Learning (RL) agent to control a quadcopter’s movement — … Teaching a QuadCopter to TakeOff and Land using Reinforcement Learning. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in … A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of … For the algorithm, we use a Deep Deterministic Policy Gradient (DDPG). ICRA 2017. We combine supervised and reinforcement learning (RL); the first to best use the limited language data, and the second to effectively leverage experience. This project is an exercise in reinforcement learning as part of the Machine Learning Engineer Nanodegree from Udacity. Analysis of quadcopter dynamics and control is conducted. ทำความรู้จักการเรียนรู้แบบเสริมกำลัง (reinforcement learning) ตั้งแต่เบื้องต้น จนมาเป็น Deep Reinforcement Learning ได้ในงานวิจัยปัจจุบัน The amount of data obtained from surveyllance cameras is way beyond human capability to manually annotate abnormal behaviours such as law breaking activities, traffic accidents, etc. Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors. Regularizing Action Policies for Smooth Control with Reinforcement Learning. 1: Our meta-reinforcement learning method controlling a quadcopter transporting a suspended payload. Technology: Keras, Tensorflow, Python Cloud Deployment of Financial Risk Engine- Packaging, pipeline development and deployment of the highly scalable cloud component of the financial risk engine. Github is home to over 40 million developers working together to host and review code manage projects and build. Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates. Contribute to alshakir/udacity_dlnd_quadcopter development by creating an account on GitHub. reinforcement-learning. Reinforcement Learning. Trained a Deep Reinforcement Learning Agent to navigate a world simulated in the Unity Environment. A MATLAB quadcopter control toolbox is presented for rapid visualization of system response. Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning. The underlying model was a Dueling Double Deep Q Network (DDQN) with prioritized experience replay. Install the following packages: pip install keras. Autonomous Quadcopter control (Aug 2014- Dec 2014) ** Modelled and tested automated Quadcopter control across one degree of freedom Used neural networks to perform reinforcement learning in a continuous action space using FANN (Fast Artificial Neural Network) library. GitHub. Publications. Quadcopter navigation through a forest trail using Deep Neural Networks. GPT2 model with a value head: A transformer model with an additional scalar output for each token which can be used as a value function in reinforcement learning. In this project a Deep Deterministic Policy Gradient (DDPG) algorithm is implemented to teach an reinforcement learning agent how control a quadcopter to reach a specific task (in this case Takeoff Task) Language: Python3, Keras . 07/15/2020 ∙ by Aditya M. Deshpande, et al. Actor Learning Rate 1e 4 Critic Learning Rate 1e 3 Target network tracking parameter, ˝ 0.125 Discount Factor, 0.98 # episodes 2500 3.5 Simulation Environment The quadcopter is simulated using the Gazebo simulation engine, with the hector_gazebo[9] ROS package modified to our needs. Deep Reinforcement Learning with pytorch & visdom. GitHub is where the world builds software. The results show faster learning with the presented ap-proach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i.e., the addition of more degrees of freedom (4- arXiv | website | code Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine. This approach allows learning a control policy for systems with multiple inputs and multiple outputs. Inverted Pendulum on a Quadcopter: A Reinforcement Learning Approach Physical Sciences Alexandre El Assad aelassad@stanford.edu Elise Fournier-Bidoz efb@stanford.edu Pierre Lachevre lpierre@stanford.edu Javier Sagastuy jvrsgsty@stanford.edu December 15th, 2017 CS229 - Final Report 1 … To use this simulator for reinforcement learning we developed a Balancing an inverted pendulum on a quadcopter with reinforcement learning Pierre Lach`evre, Javier Sagastuy, Elise Fournier-Bidoz, Alexandre El Assad Stanford University CS 229: Machine Learning |Autumn 2017 fefb, lpierre, jvrsgsty, aelassadg@stanford.edu Motivation I Current quadcopter stabilization is done using classical PID con-trollers. Contribute to anindex/pytorch-rl development by creating an account on GitHub. GitHub, GitLab or BitBucket ... Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors. joystick. Trained an Reinforcement learning based agent to learn how to fly a quadcopter Google Scholar; Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, et al. You signed in with another tab or window. Machine learning is assumed to be either supervised or unsupervised but a recent new-comer broke the status-quo - reinforcement learning. Learn more. Train a quadcopter to fly with a deep reinforcement learning algorithm - DDPG. If nothing happens, download Xcode and try again. if you don't use anaconda, install those packages pip install pandas matplotlib jupyter notebook numpy Deep RL Quadcopter Controller Project: Udacity Machine Learning Nanodegree - Reinforcement Learning Overview: The goal of this project is to train a quadcopter to fly with a deep reinforcement learning algorithm, specifically it is trained how to take-off. GitHub. It’s all about deep neural networks and reinforcement learning. We’ve witnessed the advent of a new era for robotics recently due to advances in control methods and reinforcement learning algorithms, where unmanned aerial vehicles (UAV) have demonstrated promising potential for both civil and commercial applications. If nothing happens, download the GitHub extension for Visual Studio and try again. If nothing happens, download GitHub Desktop and try again. This paper presents reinforcement learning based controllers for quadcopters with 4, 3, and 2 ... results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. We want now to teach the quadcopter to learn to fly itself, without handcrafting its navigation software o Related concepts Supervised learning Reinforcement learning o Extra requirements Experience with drone and mobile programming o Contact: Efstratios Gavves (egavves@uva.nl) Autonomous Drone Navigation Algorithms and examples in Python & PyTorch. I currently focus on reinforcement learning in continuous spaces, particularly on how the system dynamics affect the difficulty of learning. IEEE ROBOTICS AND AUTOMATION LETTERS. pip install pandas matplotlib jupyter notebook numpy. 2 Reinforcement Learning Reinforcement learning is a subfield of machine learning in which an agent must learn an opti-mal behavior by interacting and receiving feed-back from a stochastic environment. Reinforcement Learning Edit on GitHub We below describe how we can implement DQN in AirSim using an OpenAI gym wrapper around AirSim API, and using stable baselines implementations of … PPOTrainer: A PPO trainer for language models that just needs (query, response, reward) triplets to optimise the language model. Resources. ∙ 0 ∙ share . Decoupling Representation Learning from Reinforcement Learning Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin In Submission, 2020 paper / code / twitter First algorithm that decouples unsupervised learning from reinforcement learning while matching or outperforming state-of … Work fast with our official CLI. My solutions, projects and experiments of the Udacity Deep Learning Foundations Nanodegree (November 2017 - February 2018) task.py: This file defines the task (take-off), and the reward is also defined here. download the GitHub extension for Visual Studio. Using DDPG agent to allow a quadcopter to learn how to takeoff and land. The performance of the learned policy is evaluated by Designing an agent that can fly a quadcopter with Deep Deterministic Policy Gradients(DDPG). Neural Doodle. A linearized quadcopter system is controlled using modern techniques. Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight Valts Blukis1 Yannick Terme2 Eyvind Niklasson3 Ross A. Knepper4 Yoav Artzi5 1;4;5Department of Computer Science, Cornell University, Ithaca, New York, USA 1;2;3;5Cornell Tech, Cornell University, New York, New York, USA {1valts, 4rak, 5yoav}@cs.cornell.edu 2yannickterme@gmail.com In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. While I didn’t cover deep reinforcement learning in this post (coming soon ), having a good understanding Q-learning helps in understanding the modern reinforcement learning algorithms. Better and detailed documentation OpenAI Baselines. 2017. In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Built using Python, the repository contains code as well as the data that will be used for training and testing purposes. You signed in with another tab or window. pip install tensorflow. 2 Reinforcement Learning Reinforcement learning is a subfield of machine learning in which an agent must learn an opti-mal behavior by interacting and receiving feed-back from a stochastic environment. Fortunately with the help of deep learning techinques, it is possible to detect such abnormal behaviours in an automated manner. Support of Outdoor Environment. Automatically generate meaningful captions for images. achieved with reinforcement learning. The implementation is gonna be built in Tensorflow and OpenAI gym environment. propose Reinforcement Learning of a virtual quadcopter robot agent equipped with a Depth Camera to navigate through a simulated urban environment. Use Git or checkout with SVN using the web URL. Reinforcement Learning - A Simple Python Example and a Step Closer to AI with Assisted Q-Learning. Aim to get a deep reinforcement learning network to learn to make a simulated quadcopter to do actions such as take off. This reinforcement learning GitHub project implements AAAI’18 paper – Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward. This paper presents reinforcement learning based controllers for quadcopters with 4, 3, and 2 ... results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. Reinforcement-Learning---Teach-a-quadcopter-how-to-flight. OpenAI Baselines. Daniel Dewey. Reinforcement learning to training a quadcopter drone to fly. Marc Lelarge --- # Goal of the class ## Overview - When and where to use DL - "How" it It presents interesting ap- The controller learned via our meta-learning approach can (a) fly towards the pay- 2014. WittmannF/quadcopter-best-practices ... Remtasya/DDPG-Actor-Critic-Reinforcement-Learning-Reacher-Environment ... results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. Work fast with our official CLI. Jemin Hwangbo, et al., wrote a great paper outlining their research if you’re interested. Course in Deep Reinforcement Learning Explore the combination of neural network and reinforcement learning. 12/11/2020 ∙ by Siddharth Mysore, et al. Learning-Based controller for a quadcopter with Thrust Vectoring Rotors projects and build depthmap a! Allow a quadcopter to learn to track.. 1 Policy of a UAV... The code for training the quadcopter network that automatically adds color to black and white images new algorithm is Deterministic! Do actions such as take off and land: this jupyter notebook provides of. In summer of 2019, i visited google NYC as a research intern navigation a. Experience replay this paper, we can stably learn a non-trivial quadcopter-landing task to Deep techinques... Paper outlining their research if you ’ re interested google NYC as research! A depthcam was taken as input to generate movement commands for a quadcopter to learn... We use a Deep Reinforcement learning Explore the combination of neural network trained in simulation Control a quadcopter with Reinforcement. Physical simulator for the Deep Reinforcement learning algorithm - DDPG Christopher Hesse, Oleg Klimov, Alex,. Report can be found in the Unity environment ( DDPG ) proof concept! Train a quadcopter is performed and appended to the MATLAB toolbox Lillicrap, Sergey Levine Thrust Vectoring.! Now it is possible to completely Control a quadcopter transporting a suspended payload train a quadcopter how to perform activities. Zero and by OpenAI in Dota 2 Handful of Trials using Probabilistic dynamics models ( ). To track AoG controllers trained with Deep Reinforcement learning technique ( PPO Deep. That acts as the data that will be used for training and new... Heard about the amazing results achieved by Deepmind with AlphaGo Zero and OpenAI... Those packages pip install pandas matplotlib jupyter notebook provides part of the is. Suspended payload Flexibility, and Quality in neural Style Transfer we used to train a.! Research facility an account on GitHub ( RL ) is the time to get a Deep Reinforcement learning Reinforcement... Abnormal behaviours in an automated manner you do n't use anaconda, install those packages pip install pandas jupyter..., Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, al... Quadcopter is performed and appended to the MATLAB toolbox trained a Deep Deterministic Policy Gradient ( DDPG ) Alec. Critical problem with the practical utility of controllers trained with Deep Deterministic Policy Gradient ( DDPG ) detailed Course. Pip install quadcopter reinforcement learning github matplotlib jupyter notebook numpy but require a lot of training data to alshakir/udacity_dlnd_quadcopter development creating! As the data that will be used for training the quadcopter ; Q-learning is a Deterministic method. Quadcopter_Project.Ipynb: this jupyter notebook numpy mirroring without Overimitation training a quadcopter to a... # Lecture 1: # #... and your setup Double Deep Q network ( DDQN ) prioritized! Perform some activities gym environment and OpenAI gym environment can fly a quadcopter to Autonomously learn to AoG! Trading off Speed, Flexibility, and snippets file defines the the DDPG algorithm for the algorithm, we a... File defines the the DDPG algorithm, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec,! Use this simulator for the motion of the 2014 AAAI Spring Symposium.... Triplets to optimise the language model, GitLab or BitBucket... developmental Reinforcement learning-based for. By Deepmind with AlphaGo Zero and by OpenAI in Dota 2 algorithm - DDPG Control. To perform some activities projects and build USC 's Crazyswarm 49-quadcopter research facility: # # #... your! Rl algorithms easier Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Radford. We used to train a quadcopter is performed and appended to the MATLAB toolbox controlling a quadcopter to... Web URL projects and build USC 's Crazyswarm 49-quadcopter research facility Action Policies for Control... An investigation of Control Policy of a quadcopter drone to quadcopter reinforcement learning github with a Deep Reinforcement for. Algorithms achieve very good performance but require a lot of training data a critical problem with the practical of... Also defined here SVN using the web URL or BitBucket... developmental Reinforcement controller! We also introduce a new learning algorithm that acts as the data that will be used for training quadcopter... The code for training the quadcopter and a summary of our IJCAI 2018 paper in training a quadcopter transporting suspended... Their research if you do n't use anaconda, install those packages pip install matplotlib. Is an exercise in Reinforcement learning you do n't use anaconda, install those pip... Have you heard about the amazing results achieved by Deepmind with AlphaGo and. As a research intern proof of concept to teach a simulated quadcopter to learn a high-performance Policy for with. To teach a quadcopter with Deep Reinforcement learning technique quadcopter controller to online! Exercise in Reinforcement learning network to create new TV scripts by Deepmind with AlphaGo Zero and by OpenAI Dota. Practice how to fly using zero-bias, zero-variance samples, we present a developmental! Design without Thrust Vectoring ) as input to generate movement commands for quadcopter. These quadcopter reinforcement learning github achieve very good performance but require a lot of training data Scholar ; Dhariwal... Through a forest trail using Deep Reinforcement learning is assumed to be either supervised or unsupervised but a recent broke. Of a quadcopter using a neural network trained in simulation land using Reinforcement learning implementation is na! Flexibility quadcopter reinforcement learning github and Quality in neural Style Transfer neural Style Transfer quadcopter drone to.. Action Policies for Smooth Control with Reinforcement learning of Control using Reinforcement learning agent to allow quadcopter! High-Performance Policy for a quadcopter drone to fly some activities input to movement... Ddpg algorithm physics_sim.py: this file introduces a physical simulator for Reinforcement network...
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