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Found 6 papers, 1 papers with code
Deep Inverse Q-learning with Constraints
In this work, we introduce a novel class of algorithms that only needs to solve the MDP underlying the demonstrated behavior once to recover the expert policy.
Deep Constrained Q-learning
We analyze the advantages of Constrained Q-learning in the tabular case and compare Constrained DQN to reward shaping and Lagrangian methods in the application of high-level decision making in autonomous driving, considering constraints for safety, keeping right and comfort.
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving
The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component.
Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization
We prove that the combination of these short- and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm.
Off-policy Multi-step Q-learning
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control.
Dynamic Input for Deep Reinforcement Learning in Autonomous Driving
In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent.
