1 citations · 1 across the 5 of their papers we have counts for
5 papers
Realistic Counterfactual Explanations for Machine Learning-Controlled Mobile Robots using 2D LiDAR
Sindre Benjamin Remman, Anastasios M. Lekkas
This paper presents a novel method for generating realistic counterfactual explanations (CFEs) in machine learning (ML)-based control for mobile robots using 2D LiDAR. ML models, e…
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective
Sindre Benjamin Remman, Bjørn Andreas Kristiansen, Anastasios M. Lekkas
In this work, we use optimal control to change the behavior of a deep reinforcement learning policy by optimizing directly in the policy's latent space. We hypothesize that distinc…
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space
Sindre Benjamin Remman, Anastasios M. Lekkas
Understanding the behavior of deep reinforcement learning (DRL) agents is crucial for improving their performance and reliability. However, the complexity of their policies often m…
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning
Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas
We investigate the effect of including domain knowledge about a robotic system's causal relations when generating explanations. To this end, we compare two methods from explainable…
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations
Sindre Benjamin Remman, Anastasios M. Lekkas
This paper deals with robotic lever control using Explainable Deep Reinforcement Learning. First, we train a policy by using the Deep Deterministic Policy Gradient algorithm and th…