195,412 research outputs found
Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning
Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulting performance. Learning techniques are capable of adapting the model and providing better policies asymptotically compared to cooperative planners, yet they often violate the safety conditions of the system due to their exploratory nature. Moreover they frequently require an impractically large number of interactions to perform well. This paper introduces the intelligent Cooperative Control Architecture (iCCA) as a framework for combining cooperative planners and reinforcement learning techniques. iCCA improves the policy of the cooperative planner, while reduces the risk and sample complexity of the learner. Empirical results in gridworld and task assignment for fuel-limited UAV domains with problem sizes up to 9 billion state-action pairs verify the advantage of iCCA over pure learning and planning strategies
Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles
For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human
brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental
perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is
supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical self-driving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic
framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence.
This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action
planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to
inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach.
Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure
Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles
MenciĂłn Internacional en el tĂtulo de doctorFor autonomous agents to coexist with the real world, it is essential to anticipate the dynamics
and interactions in their surroundings. Autonomous agents can use models of the human
brain to learn about responding to the actions of other participants in the environment
and proactively coordinates with the dynamics. Modeling brain learning procedures is
challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant
intents. A neglected problem has long been understanding and processing environmental
perception data from the multisensorial information referring to the cognitive psychology
level of the human brain process. The key to solving this problem is to construct a computing
model with selective attention and self-learning ability for autonomous driving, which is
supposed to possess the mechanism of memorizing, inferring, and experiential updating,
enabling it to cope with the changes in an external world. Therefore, a practical selfdriving
approach should be open to more than just the traditional computing structure of
perception, planning, decision-making, and control. It is necessary to explore a probabilistic
framework that goes along with human brain attention, reasoning, learning, and decisionmaking
mechanism concerning interactive behavior and build an intelligent system inspired
by biological intelligence.
This thesis presents a multi-modal self-awareness module for autonomous driving systems.
The techniques proposed in this research are evaluated on their ability to model proper driving
behavior in dynamic environments, which is vital in autonomous driving for both action
planning and safe navigation. First, this thesis adapts generative incremental learning to
the problem of imitation learning. It extends the imitation learning framework to work
in the multi-agent setting where observations gathered from multiple agents are used to
inform the training process of a learning agent, which tracks a dynamic target. Since
driving has associated rules, the second part of this thesis introduces a method to provide
optimal knowledge to the imitation learning agent through an active inference approach.
Active inference is the selective information method gathering during prediction to increase a
predictive machine learning model’s prediction performance. Finally, to address the inference
complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and
active inference methods inspired by the brain learning procedure.Programa de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y Automática por la Universidad Carlos III de MadridPresidente: Marco Carli.- Secretario: VĂctor González Castro.- Vocal: Nicola Conc
Multi-Agent Navigation with Reinforcement Learning Enhanced Information Seeking
Multi-agent robotic networks allow simultaneous observations at different positions while avoiding a single point of failure, which is essential for emergency and time-critical applications. Autonomous navigation is vital to the task accomplishment of a multi-agent network in challenging global navigation satellite systems (GNSS)-denied environments. In these environments, agents can rely on inter-agent measurements for self-positioning. In addition, agents can conduct information seeking, i.e., they can proactively adapt their formation to enrich themselves with position information.
Classical signal processing tools can efficiently exploit the knowledge of system and measurement models, but are not applicable for long-term objectives. On the other hand, data-driven approaches like reinforcement learning (RL) are suitable for long-term action planning but have to face the critical curse of dimensionality.
In this paper, we propose a multi-agent navigation scheme with RL-enhanced information seeking, which simultaneously takes advantage of model-based and data-driven approaches to collaboratively accomplish challenging objectives while exploring a GNSS-denied environment
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Object-based approaches for learning action-conditioned dynamics has
demonstrated promise for generalization and interpretability. However, existing
approaches suffer from structural limitations and optimization difficulties for
common environments with multiple dynamic objects. In this paper, we present a
novel self-supervised learning framework, called Multi-level Abstraction
Object-oriented Predictor (MAOP), which employs a three-level learning
architecture that enables efficient object-based dynamics learning from raw
visual observations. We also design a spatial-temporal relational reasoning
mechanism for MAOP to support instance-level dynamics learning and handle
partial observability. Our results show that MAOP significantly outperforms
previous methods in terms of sample efficiency and generalization over novel
environments for learning environment models. We also demonstrate that learned
dynamics models enable efficient planning in unseen environments, comparable to
true environment models. In addition, MAOP learns semantically and visually
interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial
Intelligence (AAAI), 202
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ask whether it is feasible and truly beneficial to
combine rich probabilistic models with a closer approximation to fully Bayesian
planning. First, we use a collection of counterexamples to show formal problems
with the over-optimism inherent in Thompson sampling. Then we leverage
state-of-the-art techniques in efficient Bayes-adaptive planning and
non-parametric Bayesian methods to perform qualitatively better than both
existing conventional algorithms and Thompson sampling on two contextual
bandit-like problems.Comment: 11 pages, 11 figure
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