Most of the games that machines can now challenge humans in are strategic, but slow: Chess, Go and poker, unless played in very specific settings, have no time constraints on player moves. That is what has made the work of research group OpenAI, in online team brawler Dota 2 - which requires real-time decision-making between potentially dozens of choices in a single frame - so different. OpenAI's bots, the OpenAI Five, went head-to-head against teams of professional players at Dota 2's annual championship, The International, this August. Although the bots lost, the matches provided an insight into how reinforcement learning is changing the game when it comes to artificial intelligence. It's safe to say that AI has a reputation in gaming: many players consider a match to be an instant loss if they have to play with a bot, and a disconnect is often accompanied by "GG".

Traditional wisdom in generative modeling literature is that spurious samples that a model can generate are errors and they should be avoided. Recent research, however, has shown interest in studying or even exploiting such samples instead of eliminating them. In this paper, we ask the question whether such samples can be eliminated all together without sacrificing coverage of the generating distribution. For the class of models we consider, we experimentally demonstrate that this is not possible without losing the ability to model some of the test samples. While our results need to be confirmed on a broader set of model families, these initial findings provide partial evidence that spurious samples share structural properties with the learned dataset, which, in turn, suggests they are not simply errors but a feature of deep generative nets.

From the word'reinforcement' we get the idea of building patterns or belief system with positive feedback. With reinforcement learning we aim to create algorithms that helps an agent in achieving maximum performance in a given environment with proper rewards. Let's consider two situations giving an abstract insight for RL. Let's first define concept of agent and environment for understanding technical details about RL. Environment is the universe of agent which changes state of agent with given action performed on it.

This week, Elon Musk praised the work of OpenAI after a team of five neural networks had defeated five humans, who ranked in the top 99.95 percentile of players worldwide, in the popular game Dota 2. The five bots had learned the game by playing against itself at a rate of a staggering 180 years per day. The game requires strong teamwork among the five players and, therefore, the achievement is quite remarkable and more evidence that artificial intelligence (AI) is rapidly becoming more advanced. However, directly after the five bots beat the five humans 2-1, Musk cautioned for the power of AI by urging that OpenAI should focus on AI that works with humans, instead of against humans. His statement is in line with his previous warnings for AI, which Musk believes could result in a robot dictatorship or an AI-arms race amongst superpowers that could be the most plausible cause for World War III. With artificial intelligence becoming increasingly sophisticated, also the warnings against AI become more pervasive, and the question remains then, is AI good or bad?

Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of.

Last month, a team of self-taught AI gamers lost spectacularly against human professionals in a highly-anticipated galactic melee. Taking place as part of the International Dota 2 Championships in Vancouver, Canada, the game showed that in broader strategic thinking and collaboration, humans still remain on top. The AI was a series of algorithms developed by the Elon Musk-backed non-profit OpenAI. Collectively dubbed the OpenAI Five, the algorithms use reinforcement learning to teach themselves how to play the game--and collaborate with each other--from scratch. Unlike chess or Go, the fast-paced multi-player Dota 2 video game is considered much harder for computers.

He said the main part of its RL policy is implemented by a 4,000 dimensional LSTM network, which has roughly 100 million (10 9) parameters .He also said that in terms of Flops, it is like a honeybee brain. This means the OpenAI Dota2 bot has roughly the same number of parameters as honeybee brain (Assumption: real synapse weight can be represented by 1 Byte of memory, which is often the case in artificial neural network systems). If the former is more intelligent, it suggests that modern machine learning algorithm is more capable than natural selection at turning a certain size of parameters into intelligence. It is probable that the same algorithm can turn them into super-mice or super-cat intelligence.

Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes. We propose a new semi-supervised deep generative model that explicitly models noisy labels, called the Mislabeled VAE (M-VAE). The M-VAE can perform better than existing deep generative models which do not account for label noise. Additionally, the derivation of M-VAE gives new theoretical insights into the popular M1+M2 semi-supervised model.

In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid images, solved the blurred-image problem of VAEs. Compared with flow-based models such as Glow, f-VAE is more lightweight and converges faster, achieving the same performance under smaller-size architecture. Recently, deep generative models has been widely studied and developed. Outside of Generative Adversarial Networks (GANs) (Goodfellow et al. 2014), Variational Autoencoders (VAEs) (Kingma and Welling 2013) and flow-based models (Dinh, Krueger, and Bengio 2014; Dinh, Sohldickstein, and Bengio 2016) are two distinct kinds of competitive generative models. They have their own advantages and disadvantages, and we try to integrate them to a new model.

Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data. These representations, however, tend to much distort relationships between points, i.e. pairwise distances tend to not reflect semantic similarities well. This renders unsupervised tasks, such as clustering, difficult when working with the latent representations. We demonstrate that taking the geometry of the generative model into account is sufficient to make simple clustering algorithms work well over latent representations. Leaning on the recent finding that deep generative models constitute stochastically immersed Riemannian manifolds, we propose an efficient algorithm for computing geodesics (shortest paths) and computing distances in the latent space, while taking its distortion into account. We further propose a new architecture for modeling uncertainty in variational autoencoders, which is essential for understanding the geometry of deep generative models. Experiments show that the geodesic distance is very likely to reflect the internal structure of the data.