Reuse of Neural Modules for General Video Game Playing

A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1

Deception in Optimal Control

In this paper, we consider an adversarial scenario where one agent seeks to achieve an objective and its adversary seeks to learn the agent's intentions and prevent the agent from achieving its objective. The agent has an incentive to try to deceive the adversary about its intentions, while at the same time working to achieve its objective. The primary contribution of this paper is to introduce a mathematically rigorous framework for the notion of deception within the context of optimal control. The central notion introduced in the paper is that of a belief-induced reward: a reward dependent not only on the agent's state and action, but also adversary's beliefs. Design of an optimal deceptive strategy then becomes a question of optimal control design on the product of the agent's state space and the adversary's belief space. The proposed framework allows for deception to be defined in an arbitrary control system endowed with a reward function, as well as with additional specifications limiting the agent's control policy. In addition to defining deception, we discuss design of optimally deceptive strategies under uncertainties in agent's knowledge about the adversary's learning process. In the latter part of the paper, we focus on a setting where the agent's behavior is governed by a Markov decision process, and show that the design of optimally deceptive strategies under lack of knowledge about the adversary naturally reduces to previously discussed problems in control design on partially observable or uncertain Markov decision processes. Finally, we present two examples of deceptive strategies: a "cops and robbers" scenario and an example where an agent may use camouflage while moving. We show that optimally deceptive strategies in such examples follow the intuitive idea of how to deceive an adversary in the above settings

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications

Advancing the Applicability of Reinforcement Learning to Autonomous Control

Mit dateneffizientem Reinforcement Learning (RL) konnten beeindruckendeErgebnisse erzielt werden, z.B. für die Regelung von Gasturbinen. In derPraxis erfordert die Anwendung von RL jedoch noch viel manuelle Arbeit, wasbisher RL für die autonome Regelung untauglich erscheinen ließ. Dievorliegende Arbeit adressiert einige der verbleibenden Probleme, insbesonderein Bezug auf die Zuverlässigkeit der Policy-Erstellung. Es werden zunächst RL-Probleme mit diskreten Zustands- und Aktionsräumenbetrachtet. Für solche Probleme wird häufig ein MDP aus Beobachtungengeschätzt, um dann auf Basis dieser MDP-Schätzung eine Policy abzuleiten. DieArbeit beschreibt, wie die Schätzer-Unsicherheit des MDP in diePolicy-Erstellung eingebracht werden kann, um mit diesem Wissen das Risikoeiner schlechten Policy aufgrund einer fehlerhaften MDP-Schätzung zuverringern. Außerdem wird so effiziente Exploration sowie Policy-Bewertungermöglicht. Anschließend wendet sich die Arbeit Problemen mit kontinuierlichenZustandsräumen zu und konzentriert sich auf auf RL-Verfahren, welche aufFitted Q-Iteration (FQI) basieren, insbesondere Neural Fitted Q-Iteration(NFQ). Zwar ist NFQ sehr dateneffizient, jedoch nicht so zuverlässig, wie fürdie autonome Regelung nötig wäre. Die Arbeit schlägt die Verwendung vonEnsembles vor, um die Zuverlässigkeit von NFQ zu erhöhen. Es werden eine Reihevon Möglichkeiten der Ensemble-Nutzung entworfen und evaluiert. Bei allenbetrachteten RL-Problemen sorgen Ensembles für eine zuverlässigere Erstellungguter Policies. Im nächsten Schritt werden Möglichkeiten der Policy-Bewertung beikontinuierlichen Zustandsräumen besprochen. Die Arbeit schlägt vor, FittedPolicy Evaluation (FPE), eine Variante von FQI für Policy Evaluation, mitanderen Regressionsverfahren und/oder anderen Datensätzen zu kombinieren, umein Maß für die Policy-Qualität zu erhalten. Experimente zeigen, dassExtra-Tree-FPE ein realistisches Qualitätsmaß für NFQ-generierte Policies liefernkann. Schließlich kombiniert die Arbeit Ensembles und Policy-Bewertung, um mit sichändernden RL-Problemen umzugehen. Der wesentliche Beitrag ist das EvolvingEnsemble, dessen Policy sich langsam ändert, indem alte, untaugliche Policiesentfernt und neue hinzugefügt werden. Es zeigt sich, dass das EvolvingEnsemble deutlich besser funktioniert als einfachere Ansätze.With data-efficient reinforcement learning (RL) methods impressive resultscould be achieved, e.g., in the context of gas turbine control. However, inpractice the application of RL still requires much human intervention, whichhinders the application of RL to autonomous control. This thesis addressessome of the remaining problems, particularly regarding the reliability of thepolicy generation process. The thesis first discusses RL problems with discrete state and action spaces.In that context, often an MDP is estimated from observations. It is describedhow to incorporate the estimators' uncertainties into the policy generationprocess. This information can then be used to reduce the risk of obtaining apoor policy due to flawed MDP estimates. Moreover, it is discussed how to usethe knowledge of uncertainty for efficient exploration and the assessment ofpolicy quality without requiring the policy's execution. The thesis then moves on to continuous state problems and focuses on methodsbased on fitted Q-iteration (FQI), particularly neural fitted Q-iteration(NFQ). Although NFQ has proven to be very data-efficient, it is not asreliable as required for autonomous control. The thesis proposes to useensembles to increase reliability. Several ways of ensemble usage in an NFQcontext are discussed and evaluated on a number of benchmark domains. It showsthat in all considered domains with ensembles good policies can be producedmore reliably. Next, policy assessment in continuous domains is discussed. The thesisproposes to use fitted policy evaluation (FPE), an adaptation of FQI to policyevaluation, combined with a different function approximator and/or differentdataset to obtain a measure for policy quality. Results of experiments showthat extra-tree FPE, applied to policies generated by NFQ, produces valuefunctions that can well be used to reason about the true policy quality. Finally, the thesis combines ensembles and policy assessment to derive methodsthat can deal with changing environments. The major contribution is theevolving ensemble. The policy of the evolving ensemble changes slowly as newpolicies are added and old policies removed. It turns out that the evolvingensemble approaches work considerably better than simpler approaches likesingle policies learned with recent observations or simple ensembles

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

QDQD-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations

The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learning setup with no prior information on the global state transition and local agent cost statistics. Specifically, with the agents' objective consisting of minimizing a network-averaged infinite horizon discounted cost, the paper proposes a distributed version of $Q$-learning, $\mathcal{QD}$-learning, in which the network agents collaborate by means of local processing and mutual information exchange over a sparse (possibly stochastic) communication network to achieve the network goal. Under the assumption that each agent is only aware of its local online cost data and the inter-agent communication network is \emph{weakly} connected, the proposed distributed scheme is almost surely (a.s.) shown to yield asymptotically the desired value function and the optimal stationary control policy at each network agent. The analytical techniques developed in the paper to address the mixed time-scale stochastic dynamics of the \emph{consensus + innovations} form, which arise as a result of the proposed interactive distributed scheme, are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page