Currently I'm searching for a Reinforcement Learning toolkit for autonomous driving to test the influence of several safety aspects during learning as a reward function. So far I have tested OpenAI Gym with the "Neon racer" environment, which does not provide those information. Are there any other toolkits you would suggest me for this purpose?

Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.

Not content to simply transform the worlds of energy, transportation, and space exploration, in 2015, Elon Musk founded OpenAI, a San Fransisco-based artificial intelligence (AI) research company. The non-profits' goal is to further the technology in ways that will benefit humanity as a whole, and over the past two years, they've pushed AI into new territory. Recently, several researchers from OpenAI stepped away from the company to found Embodied Intelligence, a robotics start-up with a more singular focus: propel robotic automation to a higher level. Through their previous work, the founding members of Embodied Intelligence -- former OpenAI researchers Peter Abbeel, Peter Chen, and Rocky Duan and former Microsoft researcher Tianhao Zhang -- explored the potential of robots to mimic complex human action. Now, they are now confident they can use their past experience to improve the type of robots that are currently used in industry and even in the home.

Elon Musk's artificial intelligence company created virtual robots that can sumo wrestle and play soccer. Following is a transcript of the video. These AI robots are getting physical. They may look goofy but they're smarter than you think. OpenAI's bots can teach themselves how to sumo wrestle and play soccer.

Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online Thursday is different: Its lead author is still in high school. The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net--the kind of system that tech giants use to recognize your voice or face--two years ago, at the age of 15. Inspired by reports of software mastering Atari games and the board game Go, he has since been reading research papers and building pieces of what they described.

It happened with Chess and Go, and it finally happened with eSports. Elon Musk-backed Artificial Intelligence company "OpenAI" just used a bot to wallop the best DOTA2 players in the world. To be honest, it wasn't even close. Instead of trying to program the perfect bot, OpenAI just created a bot that learned through trial and error. Over the course of playing thousands of games against itself, the bot kept the behaviors that lead to victory and shed the ones that got it killed.

Starting with the Google DeepMind paper, there has been a lot of new attention around training models to play video games. You, the data scientist/engineer/enthusiast, may not work in reinforcement learning but probably are interested in teaching neural networks to play video games. The lessons below were gleaned from working on my own implementation of the Nature paper. The lessons are aimed at people who work with data but may run into some issues with some of the non-standard approaches used in the reinforcement learning community when compared with typical supervised learning use cases. I will address both technical details of the parameters of the neural networks and the libraries involved.

We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over one million cells, whereas competing algorithms do not. Even for smaller datasets, for several tasks, the proposed procedure outperforms state-of-the-art methods like ZIFA and ZINB-WaVE. We also extend our framework to take into account batch effects and other confounding factors and propose a natural Bayesian hypothesis framework for differential expression that outperforms tradition DESeq2.

RoboSumo, one of the latest Open AI experiments in machine learning, involves a pair of'robots' dropped into a virtual arena without even the knowledge necessary to walk, and forced to learn the tricks of sumo wrestling purely by trial and error. The video posted on YouTube shows how the bots initially clash without employing any tactics or strategy, but after a number of bouts their movements start to resemble those of human wrestlers, as they learn to dodge and attack. According to the Wired, OpenAI researchers created RoboSumo because the competition apparently generated extra complexity which "could allow faster progress than just giving reinforcement learning software more complex problems to solve alone." "When you interact with other agents you have to adapt; if you don't you'll lose," Maruan Al-Shedivat, one of the RoboSumo creators, said.

We set up competitions between multiple simulated 3D robots on a range of basic games, trained each agent with simple goals (push the opponent out of the sumo ring, reach the other side of the ring while preventing the other agent from doing the same, kick the ball into the net or prevent the other agent from doing so, and so on), then analyzed the different strategies that emerged. Agents initially receive dense rewards for behaviours that aid exploration like standing and moving forward, which are eventually annealed to zero in favor of being rewarded for just winning and losing. Despite the simple rewards, the agents learn subtle behaviors like tackling, ducking, faking, kicking and catching, and diving for the ball. Each agent's neural network policy is independently trained with Proximal Policy Optimization. To understand how complex behaviors can emerge through a combination of simple goals and competitive pressure, let's analyze the sumo wrestling task.