12 citations · 44 across the 41 of their papers we have counts for
6 papers · 1 filter
Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles
Yucheng Shi, Wenlong Wang, Xiaowen Tao +2
Dynamic scheduling of access to shared resources by autonomous systems is a challenging problem, characterized as being NP-hard. The complexity of this task leads to a combinatoria…
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Wenlong Wang, Ivana Dusparic, Yucheng Shi +2
Model-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often requires…
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
Juan C. Rosero, Ivana Dusparic, Nicolás Cardozo
Reinforcement Learning (RL) is used extensively in Autonomous Systems (AS) as it enables learning at runtime without the need for a model of the environment or predefined actions.…
Semifactual Explanations for Reinforcement Learning
Jasmina Gajcin, Jovan Jeromela, Ivana Dusparic
Reinforcement Learning (RL) is a learning paradigm in which the agent learns from its environment through trial and error. Deep reinforcement learning (DRL) algorithms represent th…
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
Jasmina Gajcin, Ivana Dusparic
Understanding how failure occurs and how it can be prevented in reinforcement learning (RL) is necessary to enable debugging, maintain user trust, and develop personalized policies…
Learning Recovery Strategies for Dynamic Self-healing in Reactive Systems
Mateo Sanabria, Ivana Dusparic, Nicolas Cardozo
Self-healing systems depend on following a set of predefined instructions to recover from a known failure state. Failure states are generally detected based on domain specific spec…