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Found 7 papers, 1 papers with code
On the Origins of Self-Modeling
Self-Modeling is the process by which an agent, such as an animal or machine, learns to create a predictive model of its own dynamics.
Full-Body Visual Self-Modeling of Robot Morphologies
Internal computational models of physical bodies are fundamental to the ability of robots and animals alike to plan and control their actions.
Visual Perspective Taking for Opponent Behavior Modeling
We suggest that visual behavior modeling and perspective taking skills will play a critical role in the ability of physical robots to fully integrate into real-world multi-agent activities.
Zero Shot Learning on Simulated Robots
Task transfer is achieved using a self-model that encapsulates the dynamics of a system and serves as an environment for reinforcement learning.
Agent Embeddings: A Latent Representation for Pole-Balancing Networks
Linearly interpolating between the latent embeddings for a good agent and a bad agent yields an agent embedding that generates a network with intermediate performance, where the performance can be tuned according to the coefficient of interpolation.
Gradient Normalization & Depth Based Decay For Deep Learning
In this paper we introduce a novel method of gradient normalization and decay with respect to depth.
Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression
Violence is a serious problems for cities like Chicago and has been exacerbated by the use of social media by gang-involved youths for taunting rival gangs.
