182 citations · 874 across the 39 of their papers we have counts for
12 papers · 1 filter
On the Effective Horizon of Inverse Reinforcement Learning
Yiqing Xu, Finale Doshi-Velez, David Hsu
Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement learning or planning, over a given time horizon, to compute an approximately optimal policy fo…
LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
Mohamad H. Danesh, Panpan Cai, David Hsu
Uncertainty on human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision processes (POMDPs) offer…
Receding Horizon Inverse Reinforcement Learning
Yiqing Xu, Wei Gao, David Hsu
Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizo…
Contrastive Variational Reinforcement Learning for Complex Observations
Xiao Ma, Siwei Chen, David Hsu +1
Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environmen…
DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space
Siwei Chen, Xiao Ma, David Hsu
It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accept…
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
Xiao Ma, Peter Karkus, David Hsu +2
Deep reinforcement learning is successful in decision making for sophisticated games, such as Atari, Go, etc. However, real-world decision making often requires reasoning with part…