Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.

A Concise Introduction to Decentralized POMDPs

We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurious rewards and a phenomenon we call the "lazy agent'' problem, which arises due to partial observability. We address these problems by training individual agents with a novel value-decomposition network architecture, which learns to decompose the team value function into agent-wise value functions.

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

Deep Q-learning from Demonstrations

We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurious rewards and a phenomenon we call the "lazy agent" problem, which arises due to partial observability. We address these problems by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions. We perform an experimental evaluation across a range of partially-observable multi-agent domains and show that learning such value-decompositions leads to superior results, in particular when combined with weight sharing, role information and information channels.

Value-Decomposition Networks For Cooperative Multi-Agent Learning

Q-learning in single-agent environments is known to converge in the limit given sufficient exploration. The same algorithm has been applied, with some success, in multi-agent environments, where traditional analysis techniques break down. Using established dynamical systems methods, we derive and study an idealization of Q-learning in 2-player 2-action repeated general-sum games. In particular, we address the discontinuous case of e-greedy exploration and use it as a proxy for value-based algorithms to highlight a contrast with existing results in policy search. Analogously to previous results for gradient ascent algorithms, we provide a complete catalog of the convergence behavior of the e-greedy Q-learning algorithm by introducing new subclasses of these games. We identify two subclasses of Prisoner's Dilemma-like games where the application of Q-learning with e-greedy exploration results in higher-than-Nash average payoffs for some initial conditions.

/pdf/classes-of-multiagent-q-learning-dynamics-with-epsilon-23pk9qha6n.pdf

Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration

Reward shaping is a well-known technique applied to help reinforcement-learning agents converge more quickly to near-optimal behavior In this paper, we introduce social reward shaping, which is reward shaping applied in the multiagent-learning framework We present preliminary experiments in the iterated Prisoner's dilemma setting that show that agents using social reward shaping appropriately can behave more effectively than other classical learning and non-learning strategies In particular, we show that these agents can both lead---encourage adaptive opponents to stably cooperate---and follow---adopt a best-response strategy when paired with a fixed opponent---where better known approaches achieve only one of these objectives

Social reward shaping in the prisoner's dilemma

Q-learning is a simple, powerful algorithm for behavior learning. It was derived in the context of single agent decision making in Markov decision process environments, but its applicability is much broader—in experiments in multiagent environments, Q-learning has also performed well. Our preliminary analysis using dynamical systems finds that Qlearning’s indirect control of behavior via estimates of value contributes to its beneficial performance in general-sum 2player games like the Prisoner’s Dilemma.

https://www.eecs.wsu.edu/~taylorm/ALA09/13.pdf

Q-learning in Two-Player Two-Action Games

Although household devices and home appliances function more and more as network-connected computers, they don't provide programming interfaces for the average user. We first identify the programming primitives and control structures necessary for the universal programming of devices. We then propose a mapping between the features necessary for the programming of devices and the existing functionality of Scratch, an educational programming language we use as a basic interface between the devices and the users. Using this modified version of the Scratch language, we demonstrate usage cases in which novice programmers can program appliances, increasing their functionality and ability to be customized. We also show how standardizing this programming paradigm can facilitate knowledge transfer to new devices. We conclude by discussing our experiences prototyping programmable appliances.

/pdf/scratchable-devices-user-friendly-programming-for-household-3h4nniuuyq.pdf

Scratchable devices: user-friendly programming for household appliances

Reward shaping is a well-known technique applied to help reinforcement-learning agents converge more quickly to nearoptimal behavior In this paper, we introduce social reward shaping, which is reward shaping applied in the multiagentlearning framework We present preliminary experiments in the iterated Prisoner’s dilemma setting that show that agents using social reward shaping appropriately can behave more effectively than other classical learning and nonlearning strategies In particular, we show that these agents can both lead —encourage adaptive opponents to stably cooperate— and follow —adopt a best-response strategy when paired with a fixed opponent— where better known approaches achieve only one of these objectives

/pdf/social-reward-shaping-in-the-prisoner-s-dilemma-short-paper-io0xoy6r7c.pdf

Social Reward Shaping in the Prisoner's Dilemma (Short Paper)

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