What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Control theory
Artificial intelligence, Mathematical optimization, Optimal control, Stochastic control and Reinforcement learning are his primary areas of study.
His work deals with themes such as Machine learning and Trajectory, which intersect with Artificial intelligence.
Differential dynamic programming, Bellman equation and Dynamic programming are the subjects of his Mathematical optimization studies.
The various areas that Evangelos A. Theodorou examines in his Optimal control study include Iterative method, Model predictive control and Nonlinear system.
His Stochastic control study incorporates themes from Kullback–Leibler divergence and Importance sampling.
He focuses mostly in the field of Reinforcement learning, narrowing it down to topics relating to Algorithmic learning theory and, in certain cases, Computational learning theory, Online machine learning and Generalization error.
His most cited work include:
- STOMP: Stochastic trajectory optimization for motion planning (442 citations)
- A Generalized Path Integral Control Approach to Reinforcement Learning (440 citations)
- Reinforcement learning of motor skills in high dimensions: A path integral approach (196 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Optimal control, Stochastic control, Artificial intelligence and Nonlinear system.
His research in Mathematical optimization intersects with topics in Sampling and Probabilistic logic.
His study explores the link between Optimal control and topics such as Trajectory that cross with problems in Computer vision.
Evangelos A. Theodorou has included themes like Stochastic differential equation, Kullback–Leibler divergence, Stochastic partial differential equation and Importance sampling in his Stochastic control study.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with his field of interest in Control.
He interconnects Control engineering and Robot, Impedance control in the investigation of issues within Reinforcement learning.
He most often published in these fields:
- Mathematical optimization (38.43%)
- Optimal control (37.50%)
- Stochastic control (30.09%)
What were the highlights of his more recent work (between 2019-2021)?
- Optimal control (37.50%)
- Nonlinear system (24.07%)
- Mathematical optimization (38.43%)
In recent papers he was focusing on the following fields of study:
Evangelos A. Theodorou mostly deals with Optimal control, Nonlinear system, Mathematical optimization, Artificial intelligence and Stochastic control.
In the field of Optimal control, his study on Differential dynamic programming overlaps with subjects such as Path integral formulation.
His study in Nonlinear system is interdisciplinary in nature, drawing from both Sampling, Dynamical systems theory and Full state feedback.
His Mathematical optimization study integrates concerns from other disciplines, such as Differentiable function, Multi-agent system and Parameterized complexity.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Trajectory optimization.
The concepts of his Stochastic control study are interwoven with issues in Stochastic differential equation, Applied mathematics and Artificial neural network.
Between 2019 and 2021, his most popular works were:
- Bayesian Learning-Based Adaptive Control for Safety Critical Systems (23 citations)
- Deep Learning Tubes for Tube MPC (11 citations)
- Imitation learning for agile autonomous driving (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Control theory
Evangelos A. Theodorou mainly focuses on Mathematical optimization, Trajectory optimization, Artificial intelligence, Optimal control and Differential dynamic programming.
His Mathematical optimization study frequently involves adjacent topics like Nonlinear system.
His Trajectory optimization research focuses on subjects like Dynamical systems theory, which are linked to Parameterized complexity, Sampling, Quadcopter and Differential game.
His research in Optimal control is mostly focused on Stochastic control.
His Stochastic control research is multidisciplinary, relying on both Artificial neural network and Actuator.
His studies deal with areas such as Robot, Visual servoing and Trajectory as well as Model predictive control.
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