… This model was integrated into deep reinforcement learning (DRL) to find strategies with …
Finally, a strategy with maximum cumulative reward will be chosen. In this study, a lane change …

… risk insensitive problem in lane change scenarios, risk assessment and DRL were
comprehensively considered in this study to prompt the agent to learn a strategy with the minimum …

… strategy to enable automated mandatory lane change maneuvers aiming at achieving the
objectives of safety, efficiency, and comfort, using PPO-based deep reinforcement learning. …

… driving and study how to learn a harmonious RL based lanechanging strategy for autonomous
vehicles by setting proper reward function for reinforcement learning. Different from fully …

… (AVs) to learn an optimal driving strategy through continuous interaction with the environment…
This paper proposes a deep reinforcement learning (DRL)-based motion planning strategy …

… deep reinforcement learning in solving the autonomous lane changing problem. In general
… to learn a high-level tactical decision making policy such that the ego car can make efficient …

… The actor network is for policy learning and the critic network is for policy evaluation. We …
deep reinforcement learning algorithm that can deal with lane change behaviors on road. Our …

… usually make lane change decisions at a given frequency, for example 1 Hz is common [6],
[7]. If the system decides to guide an ego vehicle to execute a lane change maneuver at a …

… We extend our prior work [9] that developed a policy gradient based lane change strategy …
meta reinforcement learning method which was referred to as model-agnostic meta learning (…

… on deep reinforcement learning, for automatically generating a general purpose decision
making function. A Deep … , where an agent acts in an environment and tries to learn a policy, π, …