Selective experience replay for lifelong learning
Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks,
however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To …
however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To …
Navigating occluded intersections with autonomous vehicles using deep reinforcement learning
Providing an efficient strategy to navigate safely through unsignaled intersections is a
difficult task that requires determining the intent of other drivers. We explore the …
difficult task that requires determining the intent of other drivers. We explore the …
Pomdp and hierarchical options mdp with continuous actions for autonomous driving at intersections
When applying autonomous driving technology to real-world scenarios, environmental
uncertainties make the development of decision-making algorithms difficult. Modeling the …
uncertainties make the development of decision-making algorithms difficult. Modeling the …
Dynamic Bayesian knowledge transfer between a pair of Kalman filters
M Papež, A Quinn - 2018 IEEE 28th International Workshop on …, 2018 - ieeexplore.ieee.org
Transfer learning is a framework that includes-among other topics-the design of knowledge
transfer mechanisms between Bayesian filters. Transfer learning strategies in this context …
transfer mechanisms between Bayesian filters. Transfer learning strategies in this context …
Intersection navigation under dynamic constraints using deep reinforcement learning
In this study, we present a unified motion planner with low-level controller for continuous
control of a differential drive mobile robot. Deep reinforcement agent takes 10 dimensional …
control of a differential drive mobile robot. Deep reinforcement agent takes 10 dimensional …