By Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)
This booklet constitutes the completely refereed post-workshop lawsuits of the eighth eu Workshop on studying Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised complete workshop papers provided have been conscientiously reviewed and chosen for inclusion within the booklet. additionally integrated are invited complete papers. one of the themes addressed are map construction for robotic navigation, multi-task reinforcement studying, neural community ways, example-based studying, located brokers, making plans maps for cellular robots, direction discovering, self sustaining robots, and biologically encouraged approaches.
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Extra resources for Advances in Robot Learning: 8th European Workshop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings
So far, we have considered learning tasks in which the agent-environment interaction goes on continually without limit. The above deﬁnition of Vπ (s) can be applied to episodic tasks (trials) as well. Episode termination corresponds to entering a special absorbing state that transitions only to itself and that generates only rewards of zero. The function V can be deﬁned recursively  as: Vπ (s) = R(s, a) + γ T (s, a, s )Vπ (s ). , a policy π that maximises Vπ (s) for all s ∈ S. We shall use π ∗ to refer to an optimal policy for an MDP, and express the optimal value and Q functions as V (s) and Q(s, a).
In the presentation, we focus on a description of the algorithm, but do not give a detailed quantitative comparison of the learning methods. The paper is organised as follows. In Section 2, we introduce reinforcement learning. We provide a formal framework for learning from reinforcement and summarise methods for ﬁnding an optimal policy. In Section 3, we critically examine previous work on learning with adaptive state spaces, introduce a criterion for the distinction of states, and discuss the main challenges of incremental learning.
The robot’s task is to navigate in a controlled environment and to collect objects using its gripper. Our aim is to build a control system that enables the robot to learn incrementally and to adapt to changes in the environment. The former is known as multi-task learning, the latter is usually referred to as continual ‘lifelong’ learning. First, we emphasize the connection between adaptive state-space quantisation and continual learning. Second, we describe a novel method for multi-task learning in reinforcement environments.