424 citations · 860 across the 53 of their papers we have counts for
7 papers · 1 filter
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning
Rujie Zhong, Duohan Zhang, Lukas Schäfer +2
Reinforcement learning (RL) algorithms are often categorized as either on-policy or off-policy depending on whether they use data from a target policy of interest or from a differe…
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens. Agents must learn an ac…
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
Josiah P. Hanna, Arrasy Rahman, Elliot Fosong +5
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When the…
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
Lukas Schäfer, Filippos Christianos, Josiah P. Hanna +1
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong depe…
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability
Shangmin Guo, Yi Ren, Kory Mathewson +3
Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks. We…
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving
Cillian Brewitt, Balint Gyevnar, Samuel Garcin +1
It is important for autonomous vehicles to have the ability to infer the goals of other vehicles (goal recognition), in order to safely interact with other vehicles and predict the…
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Filippos Christianos, Georgios Papoudakis, Arrasy Rahman +1
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents. Parameter sharing between ag…