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Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge.
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds.
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.
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