In recent times, imitation is not enough rl also inefficient has become increasingly relevant in various contexts. [2212.11419] Imitation Is Not Enough: Robustifying Imitation with .... In this paper, we show how imitation learning combined with reinforcement learning using simple rewards can substantially improve the safety and reliability of driving policies over those learned from imitation alone. Imitation Is Not Enough: Robustifying Imitation with ... This paper presents a method that combines imitation learning with reinforcement learning using simple rewards to substantially improve the safety and reliability of driving policies over those learned from imitation alone.
RL improves safety and robustness, especially in rare and challenging scenarios in the absence of abundant data. Moreover, relying on RL alone is problematic as it heavily depends on reward design, and this is still an open challenge. Paper page - Imitation Is Not Enough: Robustifying Imitation with .... Bibliographic details on Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios.
如何评价Imitation is not enough, RL alsoinefficient? Imitation through Neural Network is not enough, while via Principle-based Optimization as safety guarantees, works… In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under...
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