Publications (5)
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine +2
A highly desirable property of a reinforcement learning (RL) agent -- and a major difficulty for deep RL approaches -- is the ability to generalize policies learned on a few tasks…
Multi-Preference Actor Critic
Ishan Durugkar, Matthew Hausknecht, Adith Swaminathan +1
Policy gradient algorithms typically combine discounted future rewards with an estimated value function, to compute the direction and magnitude of parameter updates. However, for m…
Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy
Bram Grooten, Patrick MacAlpine, Kaushik Subramanian +2
Generalization to unseen environments is a significant challenge in the field of robotics and control. In this work, we focus on contextual reinforcement learning, where agents act…
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +39
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…