5 papers
Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning
Hoda Yamani, Henry Williams, Bruce A. MacDonald
Sample efficiency is a central challenge in reinforcement learning (RL), particularly in image-based domains where agents must learn from high-dimensional visual inputs. Traditiona…
Repetition as Reinforcement: Enhancing Sample Efficiency via Instant Episode Repetition in Reinforcement Learning
Hoda Yamani, Yuning Xing, Koen van Rijnsoever +2
Repetition is a fundamental mechanism in human learning, where revisiting successful experiences strengthens memory, consolidates skills, and improves future performance. Motivated…
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images
Zhichao Zheng, Henry Williams, Bruce A MacDonald
Monocular depth estimation is a rudimentary task in robotic perception. Recently, with the development of more accurate and robust neural network models and different types of data…
Reward Prediction Error Prioritisation in Experience Replay: The RPE-PER Method
Hoda Yamani, Yuning Xing, Lee Violet C. Ong +2
Reinforcement Learning algorithms aim to learn optimal control strategies through iterative interactions with an environment. A critical element in this process is the experience r…
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks
David Valencia, Henry Williams, Yuning Xing +3
Reinforcement Learning (RL) has been widely used to solve tasks where the environment consistently provides a dense reward value. However, in real-world scenarios, rewards can ofte…