In this tutorial, I will give an overview of the TensorFlow 2.x features through the lens of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent, solving the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.x, I will do my best to make DRL approachable as well, including a birds-eye overview of the field. In fact, since the main focus of the 2.x release is making life easier for the developers, it's a great time to get into DRL with TensorFlow. For example, the source code for this blog post is under 150 lines, including comments! Code is available on GitHub here and as a notebook on Google Colab here.

Learning and motivation are driven by internal and external rewards. Many of our day-to-day behaviours are guided by predicting, or anticipating, whether a given action will result in a positive (that is, rewarding) outcome. The study of how organisms learn from experience to correctly anticipate rewards has been a productive research field for well over a century, since Ivan Pavlov's seminal psychological work. In his most famous experiment, dogs were trained to expect food some time after a buzzer sounded. These dogs began salivating as soon as they heard the sound, before the food had arrived, indicating they'd learned to predict the reward.

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

Model-free reinforcement learning algorithms combined with value function approximation have recently achieved impressive performance in a variety of application domains, including games and robotics. However, the theoretical understanding of such algorithms is limited, and existing results are largely focused on episodic or discounted Markov decision processes (MDPs). In this work, we present adaptive approximate policy iteration (AAPI), a learning scheme which enjoys a O(T^{2/3}) regret bound for undiscounted, continuing learning in uniformly ergodic MDPs. This is an improvement over the best existing bound of O(T^{3/4}) for the average-reward case with function approximation. Our algorithm and analysis rely on adversarial online learning techniques, where value functions are treated as losses. The main technical novelty is the use of a data-dependent adaptive learning rate coupled with a so-called optimistic prediction of upcoming losses. In addition to theoretical guarantees, we demonstrate the advantages of our approach empirically on several environments.

Mixed Cooperation and competition are the actual scenarios of deploying multi-robot systems, such as the multi-UAV/UGV teaming for tracking criminal vehicles and protecting important individuals. Types and the total number of robot are all important factors that influence mixed cooperation quality. In various real-world environments, such as open space, forest, and urban building clusters, robot deployments have been influenced largely, as different robots have different configurations to support different environments. For example, UGVs are good at moving on the urban roads and reach the forest area while UAVs are good at flying in open space and around the high building clusters. However, it is challenging to design the collective behaviors for robot cooperation according to the dynamic changes in robot capabilities, working status, and environmental constraints. To solve this question, we proposed a novel proficiency-aware mixed environment multi-agent deep reinforcement learning (Mix-DRL). In Mix-DRL, robot capability and environment factors are formalized into the model to update the policy to model the nonlinear relations between heterogeneous team deployment strategies and the real-world environmental conditions. Mix-DRL can largely exploit robot capability while staying aware of the environment limitations. With the validation of a heterogeneous team with 2 UAVs and 2 UGVs in tasks, such as social security for criminal vehicle tracking, the Mix-DRL's effectiveness has been evaluated with $14.20\%$ of cooperation improvement. Given the general setting of Mix-DRL, it can be used to guide the general cooperation of UAVs and UGVs for multi-target tracking.

A simple and natural algorithm for reinforcement learning is Monte Carlo Exploring States (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and the policy is improved by choosing actions that maximize the current estimate of the Q-function. Exploration is performed by "exploring starts", that is, each episode begins with a randomly chosen state and action and then follows the current policy. Establishing convergence for this algorithm has been an open problem for more than 20 years. We make headway with this problem by proving convergence for Optimal Policy Feed-Forward MDPs, which are MDPs whose states are not revisited within any episode for an optimal policy. Such MDPs include all deterministic environments (including Cliff Walking and other gridworld examples) and a large class of stochastic environments (including Blackjack). The convergence results presented here make progress for this long-standing open problem in reinforcement learning.

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major challenges arise in online experiments where there is only one unit that receives a sequence of treatments over time. In those experiments, the treatment at a given time impacts current outcome as well as future outcomes. The aim of this paper is to introduce a reinforcement learning framework for carrying A/B testing, while characterizing the long-term treatment effects. Our proposed testing procedure allows for sequential monitoring and online updating, so it is generally applicable to a variety of treatment designs in different industries. In addition, we systematically investigate the theoretical properties (e.g., asymptotic distribution and power) of our testing procedure. Finally, we apply our framework to both synthetic datasets and a real-world data example obtained from a ride-sharing company to illustrate its usefulness.

Multi-agent reinforcement learning (MARL) has been applied to many challenging problems including two-team computer games, autonomous drivings, and real-time biddings. Despite the empirical success, there is a conspicuous absence of theoretical study of different MARL algorithms: this is mainly due to the curse of dimensionality caused by the exponential growth of the joint state-action space as the number of agents increases. Mean-field controls (MFC) with infinitely many agents and deterministic flows, meanwhile, provide good approximations to $N$-agent collaborative games in terms of both game values and optimal strategies. In this paper, we study the collaborative MARL under an MFC approximation framework: we develop a model-free kernel-based Q-learning algorithm (CDD-Q) and show that its convergence rate and sample complexity are independent of the number of agents. Our empirical studies on MFC examples demonstrate strong performances of CDD-Q. Moreover, the CDD-Q algorithm can be applied to a general class of Markov decision problems (MDPs) with deterministic dynamics and continuous state-action space.

We consider the problem of prediction by a machine learning algorithm, called learner, within an adversarial learning setting. The learner's task is to correctly predict the class of data passed to it as a query. However, along with queries containing clean data, the learner could also receive malicious or adversarial queries from an adversary. The objective of the adversary is to evade the learner's prediction mechanism by sending adversarial queries that result in erroneous class prediction by the learner, while the learner's objective is to reduce the incorrect prediction of these adversarial queries without degrading the prediction quality of clean queries. We propose a game theory-based technique called a Repeated Bayesian Sequential Game where the learner interacts repeatedly with a model of the adversary using self play to determine the distribution of adversarial versus clean queries. It then strategically selects a classifier from a set of pre-trained classifiers that balances the likelihood of correct prediction for the query along with reducing the costs to use the classifier. We have evaluated our proposed technique using clean and adversarial text data with deep neural network-based classifiers and shown that the learner can select an appropriate classifier that is commensurate with the query type (clean or adversarial) while remaining aware of the cost to use the classifier.