A Mission Based Fault Reconfiguration Framework for Spacecraft Applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97079/1/AIAA2012-2403.pd

Which is the best intrinsic motivation signal for learning multiple skills?

Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e., motivations not connected to reward-related stimuli) play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a kinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show (a) that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and (b) that the stronger the link between the IM signal and the competence of the system, the better the performance

Hierarchical reinforcement learning for trading agents

Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets