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Found 2 papers, 1 papers with code
Driving in Real Life with Inverse Reinforcement Learning
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL).
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning
Also, our algorithm is able to outperform IDQN baseline in the scenario where we have a variable number of passengers and cars in each episode.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
