#JKU #AI #Awesome
This video gives a cool overview about the possibilities AI enables us.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
0 comments
General
Credit Assignment Problem, Learning from Demonstrations, Reinforcement Learning, Reward Redistribution, RUDDER
Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards. Complex tasks can often be hierarchically decomposed into sub-tasks. A step in the Q-function can be associated with solving a sub-task, where the expectation of the return increases. RUDDER has been introduced to identify these steps and then redistribute reward to them, thus immediately giving reward if sub-tasks are solved. Since the problem of delayed rewards is mitigated, learning is considerably sped up. However, for complex tasks, current exploration strategies as deployed in RUDDER struggle with discovering episodes with high rewards. Therefore, we assume that episodes with high rewards are given as demonstrations and do not have to be discovered by exploration. Typically the number of demonstrations is small and RUDDER's LSTM model as a deep learning method does not learn well. Hence, we introduce Align-RUDDER, which is RUDDER with two major modifications. First, Align-RUDDER assumes that episodes with high rewards are given as demonstrations, replacing RUDDER's safe exploration and lessons replay buffer. Second, we replace RUDDER's LSTM model by a profile model that is obtained from multiple sequence alignment of demonstrations. Profile models can be constructed from as few as two demonstrations as known from bioinformatics. Align-RUDDER inherits the concept of reward redistribution, which considerably reduces the delay of rewards, thus speeding up learning. Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations. On the MineCraft ObtainDiamond task, Align-RUDDER is able to mine a diamond, though not frequently. Code is published on GitHub
Overcoming Catastrophic Forgetting with Context-Dependent Activations (XdA) and Synaptic Stabilization
Abstract
Overcoming Catastrophic Forgetting in neural networks is crucial to solving continuous learning problems. Deep Reinforcement Learning uses neural networks to make predictions of actions according to the current state space of an environment. In a dynamic environment, robust and adaptive life-long learning algorithms mark the cornerstone of their success. In this thesis we will examine an elaborate subset of algorithms countering catastrophic forgetting in neural networks and reflect on their weaknesses and strengths. Furthermore, we present an enhanced alternative to promising synaptic stabilization methods, such as Elastic Weight Consolidation or Synaptic Intelligence. Our method uses context-based information to switch between different pathways throughout the neural network, reducing destructive activation interference during the forward pass and destructive weight updates during the backward pass. We call this method Context-Dependent Activations (XdA). We show that XdA enhanced methods outperform basic synaptic stabilization methods and are a better choice for long task sequences.
Imagine Kara
Imagine Kara was one of the most exciting project I have been able to work on and the team has worked exceptionally hard and dedicated to make it reality! Sadly, as life goes one, some project come and some may go to enable other ideas to flourish.
I want to thank you all for your dedication, hard work and support in all aspects during this amazing experience and I wish you all the best on your new paths.
Until next time!
Yours, Marius
Seamlessly entering the crypto world with Apollon
This will provide you an overview regarding the Apollon project and how you can get started with masternodes.
How I earned almost one Bitcoin with masternodes in three weeks and how you can do the same!
Apollon is an amazing blockchain project that enables everyone to easily host and maintain masternodes. It is already competing with some Google Trends search terms such as EOS, NEO and Cardano from the top 10 list of coinmarketcap.com .
Uni Swift Project
This project is a mobile application for managing your images. HOWEVER, where most applications start to struggle when it comes to searching the right images for the right moments, here you simply find them.
Deep Learning Script
NVIDIA DIGITS offers great support for experimenting with Deep Learning and provides great integration of Caffe Script.
To improve this experience I developed a DSL for Caffe which eases the prototyping of network architectures by drastically reducing the amount of code line and simplifying the development.
All the results are available on GitHub.
The project offers a Visual Studio Code Extension on the Marketplace, which provides a transpiler from the Deep Learning Script to Caffe Script.
DLS Demo:
Deep Learning
Hi,
in this post I have added two PDF files with some important information and links related to the wide topic “Deep Learning”.
These should give you some guidence where to start and how to dig deeper.
Good luck and have fun!
Using docker for Deep Learning
Operation Phrike
Operation Phrike has been created by students from the University of Applied Sciences Upper-Austria (Bachelor Software Engineering). It is basically a simulation software for military virtual reality combat scenarios. To create an authentic battlefield experience we use the Oculus Rift in combination with the Unreal Gameing-Engine. During the simulation process the test subject, usually a military representative, is monitored by different sensors to detect his experienced stress level. The collected data will be analyzed and used to extract information, which shows how a human reacts in certain war zone situations.
1 2