Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission. If you've ever trained a puppy before, you know just how valuable food rewards can be. After little Sparky realizes that the act of rolling over instantly earns him a mouthful of peanut butter, he starts performing the trick with increased enthusiasm and speed. This type of behavioral psychology -- getting something to act a certain way so that it maximizes its rewards -- has inspired a new approach to artificial intelligence called reinforcement learning. Named one of the 10 Breakthrough Technologies of 2017 by the MIT Technology Review, this revolutionary kind of machine learning allows computers to learn new things without human intervention through the mere act of experimenting.

Machine learning algorithms, and neural networks in particular, are considered to be the cause of a new AI'revolution'. In this article I will introduce the concept of reinforcement learning but with limited technical details so that readers with a variety of backgrounds can understand the essence of the technique, its capabilities and limitations. At the end of the article, I will provide links to a few resources for implementing RL. Broadly speaking, data-driven algorithms can be categorized into three types: Supervised, Unsupervised, and Reinforcement learning. The first two are generally used to perform tasks such as image classification, detection, etc.

In statistical dialogue management, the dialogue manager learns a policy that maps a belief state to an action for the system to perform. Efficient exploration is key to successful policy optimisation. Current deep reinforcement learning methods are very promising but rely on epsilon-greedy exploration, thus subjecting the user to a random choice of action during learning. Alternative approaches such as Gaussian Process SARSA (GPSARSA) estimate uncertainties and are sample efficient, leading to better user experience, but on the expense of a greater computational complexity. This paper examines approaches to extract uncertainty estimates from deep Q-networks (DQN) in the context of dialogue management. We perform an extensive benchmark of deep Bayesian methods to extract uncertainty estimates, namely Bayes-By-Backprop, dropout, its concrete variation, bootstrapped ensemble and alpha-divergences, combining it with DQN algorithm.

A professional highlight of 2017 has been teaching A Glance at Reinforcement Learning – an introductory course I've developed. You can find the course materials on GitHub. This one day course is aimed at data scientists with a grasp of supervised machine learning but no prior understanding of reinforcement learning. Course scope – introduction to the fundamental concepts of reinforcement learning – value function methods dynamic programming, Monte Carlo, temporal difference, Q-Learning, DQN – policy gradient methods score function, REINFORCE, advantage actor-critic, AC3 – AlphaGo – practical concerns reward scaling, mistakes I've made, advice from Vlad Mnih & John Schulman – literature highlights distributional perspective, auxiliary loss functions, inverse RL I've given this course to three batches at Data Science Retreat in Berlin and once to a group of startups from Entrepreneur First in London. Each time I've had great questions, kind feedback and improved my own understanding.

We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines to approximate the posterior of the parameters. We will establish the equivalence between our proposed surrogate objective and variational inference loss. Our new algorithm achieves efficient exploration and performs well on large scale chain Markov Decision Process (MDP). Deep reinforcement learning (RL) has enjoyed numerous recent successes in video games, board games, and robotics control [17, 3, 9, 18]. Deep RL algorithms typically apply naive exploration schemes such as ɛ greedy [12, 19], directly injecting noise into actions [10], and action level entropy regularization [24].

Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.

We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks - generative and predictive - that are trained separately but employed jointly to generate novel chemical structures with the desired properties. Generative models are trained to produce chemically feasible SMILES, and predictive models are derived to forecast the desired compound properties. One example of such an approach is the broad use of Lipinski's rules of bioavailability (15, 16) to filter molecules that possess the desired bioactivity in vitro. Indeed, it has been acknowledged that the broad use of these rules has substantially reduced the failure rate in experimental ADME studies of drug candidates (17). The crucial step in many new drug discovery projects is the formulation of a well-motivated hypothesis for new lead compound generation (de novo design) or compound selection from available or synthetically feasible chemical libraries based on the available SAR data. Commonly, an interdisciplinary team of scientists generates the new hypothesis by employing computational models of drug action and relying on their expertise and medicinal chemistry intuition. Therefore, the design hypothesis is often biased towards preferred chemistry (18) or driven by model interpretation (19). Automated approaches for designing compounds with desired properties de novo have become an active field of research in the last 15 years (20, 21). In an attempt to design new compounds, both medicinal and computational chemists face virtually infinite chemical space. Great advances in both computational algorithms(24, 25), hardware, and high-throughput screening (HTS) technologies (16) notwithstanding, the size of this virtual library prohibits its exhaustive sampling and testing by systematic construction and evaluation of each individual compound. Local optimization approaches have been proposed but they do not ensure the optimal solution, as the design process converges on a local or'practical' optimum by stochastic sampling, or restrict the search to a defined section of chemical space which can be screened exhaustively (20, 26-28).

This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the distribution of the returns (return uncertainty). We identify methods to learn these distributions with deep neural networks, where we estimate parametric uncertainty with Bayesian drop-out, while return uncertainty is propagated through the Bellman equation as a Gaussian distribution. Then, we identify that both can be jointly estimated in one network, which we call the Double Uncertain Value Network. The policy is directly derived from the learned distributions based on Thompson sampling. Experimental results show that both types of uncertainty may vastly improve learning in domains with a strong exploration challenge.

We propose a multiagent distributed actor-critic algorithm for multitask reinforcement learning (MRL), named Diff-DAC. The agents are connected, forming a (possibly sparse) network. Each agent is assigned a task and has access to data from this local task only. During the learning process, the agents are able to communicate some parameters to their neighbors. Since the agents incorporate their neighbors' parameters into their own learning rules, the information is diffused across the network, and they can learn a common policy that generalizes well across all tasks. Diff-DAC is scalable since the computational complexity and communication overhead per agent grow with the number of neighbors, rather than with the total number of agents. Moreover, the algorithm is fully distributed in the sense that agents self-organize, with no need for coordinator node. Diff-DAC follows an actor-critic scheme where the value function and the policy are approximated with deep neural networks, being able to learn expressive policies from raw data. As a by-product of Diff-DAC's derivation from duality theory, we provide novel insights into the standard actor-critic framework, showing that it is actually an instance of the dual ascent method to approximate the solution of a linear program. Experiments illustrate the performance of the algorithm in the cart-pole, inverted pendulum, and swing-up cart-pole environments.

We investigate the integration of a planning mechanism into sequence-to-sequence models using attention. We develop a model which can plan ahead in the future when it computes its alignments between input and output sequences, constructing a matrix of proposed future alignments and a commitment vector that governs whether to follow or recompute the plan. This mechanism is inspired by the recently proposed strategic attentive reader and writer (STRAW) model for Reinforcement Learning. Our proposed model is end-to-end trainable using primarily differentiable operations. We show that it outperforms a strong baseline on character-level translation tasks from WMT'15, the algorithmic task of finding Eulerian circuits of graphs, and question generation from the text. Our analysis demonstrates that the model computes qualitatively intuitive alignments, converges faster than the baselines, and achieves superior performance with fewer parameters.