Multi-task learning with modular reinforcement learning

International audienceThe ability to learn compositional strategies in multi-task learning and to exert them appropriately is crucial to the development of artificial intelligence. However, there exist several challenges: (i) how to maintain the independence of modules in learning their own sub-tasks; (ii) how to avoid performance degradation in situations where modules' reward scales are incompatible; (iii) how to find the optimal composite policy for the entire set of tasks. In this paper, we introduce a Modular Reinforcement Learning (MRL) framework that coordinates the competition and the cooperation between separate modules. A selective update mechanism enables the learning system to align incomparable reward scales in different modules. Furthermore, the learning system follows a "joint policy" to calculate actions' preferences combined with their responsibility for the current task. We evaluate the effectiveness of our approach on a classic food-gathering and predator-avoidance task. Results show that our approach has better performance than previous MRL methods in learning separate strategies for sub-tasks, is robust to modules with incomparable reward scales, and maintains the independence of the learning in each module

Learning Behaviors by an Autonomous Social Robot with Motivations

In this study, an autonomous social robot is living in a laboratory where it can interact with several items (people included). Its goal is to learn by itself the proper behaviors in order to maintain its well-being at as high a quality as possible. Several experiments have been conducted to test the performance of the system. The Object Q-Learning algorithm has been implemented in the robot as the learning algorithm. This algorithm is a variation of the traditional Q-Learning because it considers a reduced state space and collateral effects. The comparison of the performance of both algorithms is shown in the first part of the experiments. Moreover, two mechanisms intended to reduce the learning session durations have been included: Well-Balanced Exploration and Amplified Reward. Their advantages are justified in the results obtained in the second part of the experiments. Finally, the behaviors learned by our robot are analyzed. The resulting behaviors have not been preprogrammed. In fact, they have been learned by real interaction in the real world and are related to the motivations of the robot. These are natural behaviors in the sense that they can be easily understood by humans observing the robot.The authors gratefully acknowledge the funds provided by the Spanish Government through the project call "Aplicaciones de los robots sociales", DPI2011-26980 from the Spanish Ministry of Economy and Competitiveness.Publicad

Multiple-goal reinforcement learning with modular sarsa(0

We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processes. According to our formulation different sub-goals are modeled as MDPs that are coupled by the requirement that they share actions. Existing reinforcement learning algorithms address similar problem formulations by first finding optimal policies for the component MDPs, and then merging these into a policy for the composite task. The problem with such methods is that policies that are optimized separately may or may not perform well when they are merged into a composite solution. Instead of searching for optimal policies for the component MDPs in isolation, our approach finds good policies in the context of the composite task

Multiple-Goal Reinforcement Learning with Modular Sarsa(0)

We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processes. According to our formulation different sub-goals are modeled as MDPs that are coupled by the requirement that they share actions. Existing reinforcement learning algorithms address similar problem formulations by first finding optimal policies for the component MDPs, and then merging these into a policy for the composite task. The problem with such methods is that policies that are optimized separately may or may not perform well when they are merged into a composite solution. Instead of searching for optimal policies for the component MDPs in isolation, our approach finds good policies in the context of the composite task

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence