A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
Variable impedance control and learning—a review
Robots that physically interact with their surroundings, in order to accomplish some tasks or
assist humans in their activities, require to exploit contact forces in a safe and proficient …
assist humans in their activities, require to exploit contact forces in a safe and proficient …
Morel: Model-based offline reinforcement learning
In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …
solely on a dataset of historical interactions with the environment. This serves as an extreme …
[كتاب][B] Machine learning in finance
MF Dixon, I Halperin, P Bilokon - 2020 - Springer
Machine learning in finance sits at the intersection of a number of emergent and established
disciplines including pattern recognition, financial econometrics, statistical computing …
disciplines including pattern recognition, financial econometrics, statistical computing …
Model-based reinforcement learning: A survey
Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
Optimization-based control for dynamic legged robots
In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to
impact emerging robotics applications from logistics, to agriculture, to home assistance. The …
impact emerging robotics applications from logistics, to agriculture, to home assistance. The …
A unified mpc framework for whole-body dynamic locomotion and manipulation
In this letter, we propose a whole-body planning framework that unifies dynamic locomotion
and manipulation tasks by formulating a single multi-contact optimal control problem. We …
and manipulation tasks by formulating a single multi-contact optimal control problem. We …
Global convergence of policy gradient methods for the linear quadratic regulator
Direct policy gradient methods for reinforcement learning and continuous control problems
are a popular approach for a variety of reasons: 1) they are easy to implement without …
are a popular approach for a variety of reasons: 1) they are easy to implement without …
Local motion phases for learning multi-contact character movements
Training a bipedal character to play basketball and interact with objects, or a quadruped
character to move in various locomotion modes, are difficult tasks due to the fast and …
character to move in various locomotion modes, are difficult tasks due to the fast and …
Solar: Deep structured representations for model-based reinforcement learning
Abstract Model-based reinforcement learning (RL) has proven to be a data efficient
approach for learning control tasks but is difficult to utilize in domains with complex …
approach for learning control tasks but is difficult to utilize in domains with complex …