Despite mystery and intrigue, the reality is that most hedge funds don't make money. This hasn't stopped a growing list of startups from trying their hands at employing machine learning to tip the scales in their favor. But Pit.ai, a new machine learning-powered hedge fund, adopted into the YC W17 class, thinks it can best Numerai, Quantopian and others with its own unique recipe for automating money making. Hedge funds employ aggressive trading strategies to "seek alpha," which is industry jargon for above market returns. These are not your standard trading shops, and over the last decade firms have gone to great lengths to seize data for information arbitrage.

When using reinforcement learning (RL) algorithms to evaluate a policy it is common, given a large state space, to introduce some form of approximation architecture for the value function (VF). The exact form of this architecture can have a significant effect on the accuracy of the VF estimate, however, and determining a suitable approximation architecture can often be a highly complex task. Consequently there is a large amount of interest in the potential for allowing RL algorithms to adaptively generate (i.e. to learn) approximation architectures. We investigate a method of adapting approximation architectures which uses feedback regarding the frequency with which an agent has visited certain states to guide which areas of the state space to approximate with greater detail. We introduce an algorithm based upon this idea which adapts a state aggregation approximation architecture on-line. Assuming $S$ states, we demonstrate theoretically that - provided the following relatively non-restrictive assumptions are satisfied: (a) the number of cells $X$ in the state aggregation architecture is of order $\sqrt{S}\ln{S}\log_2{S}$ or greater, (b) the policy and transition function are close to deterministic, and (c) the prior for the transition function is uniformly distributed - our algorithm can guarantee, assuming we use an appropriate scoring function to measure VF error, error which is arbitrarily close to zero as $S$ becomes large. It is able to do this despite having only $O(X\log_2{S})$ space complexity (and negligible time complexity). We conclude by generating a set of empirical results which support the theoretical results.

We study the use of randomized value functions to guide deep exploration in reinforcement learning. This offers an elegant means for synthesizing statistically and computationally efficient exploration with common practical approaches to value function learning. We present several reinforcement learning algorithms that leverage randomized value functions and demonstrate their efficacy through computational studies. We also prove a regret bound that establishes statistical efficiency with a tabular representation.

Have you ever experienced the dread of overhearing two people, speaking a language you don't understand, begin laughing wildly? You just have to wonder what it is they're talking about, and if it's a joke at your expense. Heck, maybe you even check your teeth to make sure you aren't walking around with half of your lunchtime ham sandwich stuck to your gums. As Wired reports, researchers at OpenAI have made some huge strides in getting bots to communicate with each other, and without actually telling them how to do so. The group published a research paper earlier this week explaining exactly how they were able to accomplish the complex task, and it's all based on reinforcement learning.

Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is done with a limited horizon per computational and real-time constraints and often also for robustness to potential model errors. However, the limited horizon leads to suboptimal performance. In this work, we consider the iterative learning setting, where the same task can be repeated several times, and propose a policy improvement scheme for MPC. The main idea is that between executions we can, offline, run MPC with a longer horizon, resulting in a hindsight plan. To bring the next real-world execution closer to the hindsight plan, our approach learns to re-shape the original cost function with the goal of satisfying the following property: short horizon planning (as realistic during real executions) with respect to the shaped cost should result in mimicking the hindsight plan. This effectively consolidates long-term reasoning into the short-horizon planning. We empirically evaluate our approach in contact-rich manipulation tasks both in simulated and real environments, such as peg insertion by a real PR2 robot.

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

The technologies making waves in 2017 include brain implants and quantum computers. Here is a list of the top 10 technologies that are expected to be prevalent this year, according to MIT. At the top of the list is behavior-reinforced artificial intelligence. Whether that's mastering the complex game of Go and beating a champion or learning to merge a self-driving car into traffic. The technology is based on reinforcement learning, documented more than a 100 years ago by psychologist Edward Thorndike.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Off-policy stochastic actor-critic methods rely on approximating the stochastic policy gradient in order to derive an optimal policy. One may also derive the optimal policy by approximating the action-value gradient. The use of action-value gradients is desirable as policy improvement occurs along the direction of steepest ascent. This has been studied extensively within the context of natural gradient actor-critic algorithms and more recently within the context of deterministic policy gradients. In this paper we briefly discuss the off-policy stochastic counterpart to deterministic action-value gradients, as well as an incremental approach for following the policy gradient in lieu of the natural gradient.