7 research outputs found
IQ-Learn: Inverse soft-Q Learning for Imitation
In many sequential decision-making problems (e.g., robotics control, game
playing, sequential prediction), human or expert data is available containing
useful information about the task. However, imitation learning (IL) from a
small amount of expert data can be challenging in high-dimensional environments
with complex dynamics. Behavioral cloning is a simple method that is widely
used due to its simplicity of implementation and stable convergence but doesn't
utilize any information involving the environment's dynamics. Many existing
methods that exploit dynamics information are difficult to train in practice
due to an adversarial optimization process over reward and policy approximators
or biased, high variance gradient estimators. We introduce a method for
dynamics-aware IL which avoids adversarial training by learning a single
Q-function, implicitly representing both reward and policy. On standard
benchmarks, the implicitly learned rewards show a high positive correlation
with the ground-truth rewards, illustrating our method can also be used for
inverse reinforcement learning (IRL). Our method, Inverse soft-Q learning
(IQ-Learn) obtains state-of-the-art results in offline and online imitation
learning settings, significantly outperforming existing methods both in the
number of required environment interactions and scalability in high-dimensional
spaces, often by more than 3x.Comment: Spotlight in NeurIPS 2021. Winner of '21 MineRL BASALT Challenge.
Website: https://div99.github.io/IQ-Lear
Learning to Detect Touches on Cluttered Tables
We present a novel self-contained camera-projector tabletop system with a
lamp form-factor that brings digital intelligence to our tables. We propose a
real-time, on-device, learning-based touch detection algorithm that makes any
tabletop interactive. The top-down configuration and learning-based algorithm
makes our method robust to the presence of clutter, a main limitation of
existing camera-projector tabletop systems. Our research prototype enables a
set of experiences that combine hand interactions and objects present on the
table. A video can be found at https://youtu.be/hElC_c25Fg8
Enhanced Network Intrusion Detection System
A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well