12,018 research outputs found
Asymmetric Actor Critic for Image-Based Robot Learning
Deep reinforcement learning (RL) has proven a powerful technique in many
sequential decision making domains. However, Robotics poses many challenges for
RL, most notably training on a physical system can be expensive and dangerous,
which has sparked significant interest in learning control policies using a
physics simulator. While several recent works have shown promising results in
transferring policies trained in simulation to the real world, they often do
not fully utilize the advantage of working with a simulator. In this work, we
exploit the full state observability in the simulator to train better policies
which take as input only partial observations (RGBD images). We do this by
employing an actor-critic training algorithm in which the critic is trained on
full states while the actor (or policy) gets rendered images as input. We show
experimentally on a range of simulated tasks that using these asymmetric inputs
significantly improves performance. Finally, we combine this method with domain
randomization and show real robot experiments for several tasks like picking,
pushing, and moving a block. We achieve this simulation to real world transfer
without training on any real world data.Comment: Videos of experiments can be found at http://www.goo.gl/b57WT
The essence of P2P: A reference architecture for overlay networks
The success of the P2P idea has created a huge diversity
of approaches, among which overlay networks, for example,
Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS,
have received specific attention from both developers and
researchers. A wide variety of algorithms, data structures,
and architectures have been proposed. The terminologies
and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable
Dual-layer network representation exploiting information characterization
In this paper, a logical dual-layer representation approach is proposed to facilitate the analysis of directed and weighted complex networks. Unlike the single logical layer structure, which was widely used for the directed and weighted flow graph, the proposed approach replaces the single layer with a dual-layer structure, which introduces a provider layer and a requester layer. The new structure provides the characterization of the nodes by the information, which they provide to and they request from the network. Its features are explained and its implementation and visualization are also detailed. We also design two clustering methods with different strategies respectively, which provide the analysis from different points of view. The effectiveness of the proposed approach is demonstrated using a simplified example. By comparing the graph layout with the conventional directed graph, the new dual-layer representation reveals deeper insight into the complex networks and provides more opportunities for versatile clustering analysis.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)
- âŠ