Machine Learning

Transfer Learning in the field of machine learning is the method of using knowledge gained while learning previous tasks on a new task. In Reinforcement Learning, one way to utilize Transfer Learning is using the knowledge gained while training an agent in a specific domain on a task with its respective reward function, on a new task in the same domain but with different reward function. Barreto et al. (2017)...

Abstract This document summarizes the efforts we made training an AI to play the popular PC game “Starcraft 2”, using Multi Agent deep reinforcement learning methods. As part of our solution we have trained several variants of agents, using Multi agent methods to complete the game, and doing so, have managed to get better results than the baseline by about 25%. Our agents were trained using different Reinforcement Learning algorithms,...

Portfolio Management is the decision-making process of allocating wealth across a set of assets, it is a fundamental problem in computational finance and has been extensively studied across several research communities. Cryptocurrencies are digital assets designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. In this work, we try to adapt...

Stock markets today have become fast moving dealing with a very high daily trade volume. It makes it difficult to be able to make reliable investments as prices are very volatile and can change rapidly. Inspired by recent advances in deep learning and the growing interest in RNN networks, we aim to use various deep learning techniques to implements models able to make reliable and accurate stock market predictions. We...

We view the neural network as a probabilistic model. In this project, we propose an online Variational Bayes approximation. In an online approach, the data points are received one at a time, and the parameters of the neural network are updated after each observation. The Bayesian framework makes an online approach to learning which is natural to implement using Bayes’ rule. The assumption of a small change in the parameters...

While todays interaction between man and machine is done many through screens a much more natural way to do so is via speech. Many industry leaders are already developing and improving algorithms that are meant to mimic and understand human language. Achieving human like performance will open a completely new way to interact with devices and offer new applications we haven’t thought of before. In this Project we test state...

We propose a lifelong learning system that utilizes knowledge obtained from previously learned skills in order to help generalize and accelerate the learning of new skills. A skill is a strategy learned by the agent in order to perform a certain task. A Multi-Task Agent is an agent that can use multiple skills. Policy Distillation is an offline learning method to create Multi-Task Agents, by using multiple copies of the...

The success of multi-task deep reinforcement learning has been limited so far and the community is currently lacking explanations. We suspect that the main reasons are instabilities between gradients that are coming from different tasks. We support these claims with results from representation learning, a powerful tool to discover properties of learning algorithms. In particular, t-SNE has been demonstrated to be useful for visualizing the learned representation of Deep Reinforcement...

Text-based games are rich virtual environments with textual interactions using mostly natural language. Since the game states are unobservable, these games are especially challenging for automatic game players. In our project, we attempt to solve a virtual sub-domain by learning corresponding control policies. We employ a deep reinforcement learning framework to jointly learn state representations and action representations, using game rewards as feedback. We approximate the Q-function using an interaction...

The novel approach of Deep Deterministic Policy Gradient (DDPG) presented in [\cite{lillicrap2015continuous}] has shown great success in tackling Reinforcement Learning (RL) problems featuring continuous state and action spaces. [\cite{hausknecht2015deep}] extended the DDPG algorithm for solving multiple parameterized continuous action space implemented in the Half-Field-Offense (HFO) RoboCup environment. In this paper we propose an alternative architecture to solve the HFO RL problem in terms of network size and training time while...

The objective of this project is to find a generic method for solving non-linear, continuous control problems, using deep learning and an Iterative Linear Quadratic Regulator controller. During the course of this project, we solved a private control problem, for which we built an algorithm that may be applied to generic control problems. In the private problem, we are given a surface, centered on which is a robotic arm with...

In DRL, we can refer to transfer learning as the ability to use knowledge gained while training an agent in one domain and applying it to the training of another agent, usually in a different domain. By transferring the weights of different parts of the networks, we sought to improve the learning rate and maximum reward achieved by the DQN algorithm.

Monte-Carlo Tree Search is a heuristic search algorithm used mainly for decision making processes. In this project, we explored the use of Monte-Carlo Tree Search in many challenging environments, both as a real-time agent, and in the learning phase. How will the vanilla algorithm contend with OpenAI Gym's challenging environments? What heuristics and optimizations can we apply to bolster the performance? Through testing, analysis and inspiration from academic papers, we...

Autism spectrum disorders (ASD) are a range of complex neurodevelopmental disorders that mainly affect behavior and cognition. It is diagnosed by symptoms in two core behavioral dimensions: persistent deficits in social communication and social interaction, and restrictive, repetitive and stereotyped patterns of behavior. ASD is known to be a multifactorial disorder; Cumulative evidence point to environmental and epigenetic changes as the major causes of autism while during the last decade...

In recent years, facial recognition systems, which are based on classifying neural networks, managed to achieve extraordinary high performances, often exceeding those of humans. However, vital drawbacks of these systems prevent them from executing significant tasks, such as recognizing a face of a person without designated training on his images, enlarging the pool of subjects the system is able to recognize, or even determine a face doesn’t belong to the...

In this work, we have implemented a version of the celebrated DQN algorithm in order to construct an intelligent controller for self-balancing an inverted pendulum, both in simulation and reality.