5 citations · 17 across the 19 of their papers we have counts for
7 papers · 1 filter
Terra Nova: A Comprehensive Challenge Environment for Intelligent Agents
Trevor McInroe
We introduce Terra Nova, a new comprehensive challenge environment (CCE) for reinforcement learning (RL) research inspired by Civilization V. A CCE is a single environment in which…
Forgetting is Everywhere
Ben Sanati, Thomas L. Lee, Trevor McInroe +4
A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data. Addressing this problem requires a principle…
Enhancing Tactile-based Reinforcement Learning for Robotic Control
Elle Miller, Trevor McInroe, David Abel +2
Achieving safe, reliable real-world robotic manipulation requires agents to evolve beyond vision and incorporate tactile sensing to overcome sensory deficits and reliance on ideali…
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius +6
As embodied autonomous systems capable of assisting humans in daily activities remain a major goal for robotics, efficient and appropriate reinforcement learning (RL) simulation te…
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
Samuel Garcin, Trevor McInroe, Pablo Samuel Castro +4
Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents. Actor-critic algorithms add further co…
PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU
Trevor McInroe, Samuel Garcin
We present PixelBrax, a set of continuous control tasks with pixel observations. We combine the Brax physics engine with a pure JAX renderer, allowing reinforcement learning (RL) e…
Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning
Weipu Zhang, Adam Jelley, Trevor McInroe +2
While deep reinforcement learning (RL) from pixels has achieved remarkable success, its sample inefficiency remains a critical limitation for real-world applications. Model-based R…