Results 1 to 10 of about 100,853 (111)
Learning Singularity Avoidance [PDF]
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to its success, this paper provides an approach, especially for non-expert users, for the system to learn the constraints contained in a set of ...
Manavalan, Jeevan +1 more
+6 more sources
Pattern of dopamine signaling during aversive events predicts active avoidance learning. [PDF]
Proc Natl Acad Sci U S A, 2019 Stelly CE +7 more
europepmc +2 more sources
Avoidance learning: a review of theoretical models and recent developments. [PDF]
Front Behav Neurosci, 2015 Krypotos AM +3 more
europepmc +2 more sources
Online Learning for Obstacle Avoidance
CoRR, 2023 We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations of uncertainty in the environment or a stationary stochastic model of uncertainty, we propose a method that is efficient to implement and provably grants instance-optimality
David Snyder +6 more
openaire +3 more sources
LEARNING TO AVOID RISKY ACTIONS [PDF]
Cybernetics and Systems, 2011 When a reinforcement learning agent executes actions that can cause frequent damage to itself, it can learn, by using Q -learning, that these actions must not be executed again. However, there are other actions that do not cause damage frequently but only once in a while, for example, risky actions such as parachuting.
María Malfaz, Miguel Angel Salichs
openaire +1 more source
PLOS Biology, 2018 Regret can be defined as the subjective experience of recognizing that one has made a mistake and that a better alternative could have been selected. The experience of regret is thought to carry negative utility. This typically takes two distinct forms: augmenting immediate postregret valuations to make up for losses, and augmenting long-term changes ...
Brian M. Sweis +2 more
openaire +4 more sources
Avoiding Jammers: A Reinforcement Learning Approach [PDF]
2020 IEEE International Radar Conference (RADAR), 2020 This paper investigates the anti-jamming performance of a cognitive radar under a partially observable Markov decision process (POMDP) model. First, we obtain an explicit expression for uncertainty of jammer dynamics, which paves the way for illuminating the performance metric of probability of being jammed for the radar beyond a conventional signal-to-
Serkan Ak, Stefan Brüggenwirth
openaire +2 more sources
Avoiding coding tricks by hyperrobust learning
Theoretical Computer Science, 1999 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Matthias Ott, Frank Stephan 0001
openaire +2 more sources
Avoidance Learning Using Observational Reinforcement Learning
CoRR, 2019 Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \textit{learning to avoid}, an objective opposite to imitation learning in some sense, where an agent ...
David Venuto +6 more
openaire +2 more sources
On Avoidance Learning with Partial Observability
CoRR, 2016 We study a framework where agents have to avoid aversive signals. The agents are given only partial information, in the form of features that are projections of task states. Additionally, the agents have to cope with non-determinism, defined as unpredictability on the way that actions are executed. The goal of each agent is to define its behavior based
openaire +2 more sources