What: Recently there has been an important research effort into modular, distributed robotics and in particular, self-reconfiguring robotics [2, 5, 8]. Issues with designing controllers for such systems range from constructing motor control primitives to ensuring cooperation between modules. For simpler tasks, such as locomotion in one direction, hand design is easy. However, as modular robots ...

This paper attempts to bridge the elds of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable eeects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected ...

This paper attempts to bridge the elds of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable eeects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected ...

Future operations involving drones are expected to result in traffic densities that orders of magnitude higher than any observed manned aviation. Current geometric conflict resolution (CR) methods have proven be very efficient at relatively moderate densities. However, densities, performance is hindered by the unpredictable emergent behaviour from neighbouring aircraft. Reinforcement learning (...

Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, etc. Although the individual agents can be programmed in advance, many tasks require that they learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. This paper gives a survey of multiag...

We analyze reinforcement learning under so-called “dynamic reinforcement”. In reinforcement learning, each agent repeatedly interacts with an unknown environment (i.e., other agents), receives a reward, and updates the probabilities of its next action based on its own previous actions and received rewards. Unlike standard reinforcement learning, dynamic reinforcement uses a combination of long ...

methods are proposed to develop idealized moment-curvature response curves of slender rectangular rc wall sections with uniformly distributed longitudinal reinforcement, using limit states, namely, cracking of concrete in tension, tensile yielding of reinforcement layers, and compression failure of concrete. it is recommended that tensile yielding of an inner layer of reinforcement is considere...

|The sparse feedback in reinforcement learning problems makes feature extraction diicult. We present importance-based feature extraction, which guides a bottom-up self-organization of feature detectors according to top-down information as to the importance of the features; we deene importance in terms of the reinforcement values expected as a result of taking diierent actions when a feature is ...

In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of autonomy of the Nao by recharging. The control con...