Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach
Using simulation models to conduct safety assessments can have several advantages as it
enables the evaluation of the safety of various design and traffic management options before …
enables the evaluation of the safety of various design and traffic management options before …
Ensemble-based deep reinforcement learning for robust cooperative wind farm control
The wake effect is the major obstacle to reaching the maximum power generation for wind
farms, since choosing the suitable wake model that satisfies both computational cost and …
farms, since choosing the suitable wake model that satisfies both computational cost and …
A cerebellum-inspired prediction and correction model for motion control of a musculoskeletal robot
It is an important issue that how to regulate the existing control models of musculoskeletal
robots to improve the ability of motion learning and generalization. In this article, based on …
robots to improve the ability of motion learning and generalization. In this article, based on …
TASAC: A twin-actor reinforcement learning framework with a stochastic policy with an application to batch process control
Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes
pose a challenge for process control. Due to the absence of accurate models and resulting …
pose a challenge for process control. Due to the absence of accurate models and resulting …
Artificial intelligence for prosthetics: Challenge solutions
Abstract In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants
were tasked with building a controller for a musculoskeletal model with a goal of matching a …
were tasked with building a controller for a musculoskeletal model with a goal of matching a …
Ace: An actor ensemble algorithm for continuous control with tree search
In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control
with a deterministic policy in reinforcement learning. In ACE, we use actor ensemble (ie …
with a deterministic policy in reinforcement learning. In ACE, we use actor ensemble (ie …
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
Robotic systems that aspire to operate in uninstrumented real-world environments must
perceive the world directly via onboard sensing. Vision-based learning systems aim to …
perceive the world directly via onboard sensing. Vision-based learning systems aim to …
Multi-critic actor learning: Teaching RL policies to act with style
Using a single value function (critic) shared over multiple tasks in Actor-Critic multi-task
reinforcement learning (MTRL) can result in negative interference between tasks, which can …
reinforcement learning (MTRL) can result in negative interference between tasks, which can …
Efficient and robust reinforcement learning with uncertainty-based value expansion
By integrating dynamics models into model-free reinforcement learning (RL) methods,
model-based value expansion (MVE) algorithms have shown a significant advantage in …
model-based value expansion (MVE) algorithms have shown a significant advantage in …
Empowering the diversity and individuality of option: Residual soft option critic framework
Extracting temporal abstraction (option), which empowers the action space, is a crucial
challenge in hierarchical reinforcement learning. Under a well-structured action space …
challenge in hierarchical reinforcement learning. Under a well-structured action space …