Hi all, I'm part of a research group at UC Berkeley focusing on the application of reinforcement learning to autonomous vehicles. We're trying to interface SUMO with OpenAi's RLLab reinforcement learning package.
In order to to facilitate running simulations with autonomous vehicles repeatedly, updating their controllers according to a new policy, we are trying to design a wrapper for sumo using the traci library. Having just started the design and implementation of our wrapper, we had a couple of questions: 1. The simulation seems to be running rather slowly when traci is attached (even though it's not actually issuing any commands, just calling simulationStep()). What is the bottleneck? My guess would be the traci-sumo connection, rather than sumo, or traci individually. Is there some way to alleviate this issue? 2. Each simulation should be started from an identical start point. Is there a way to restart the simulation without closing and reopening traci? 3. One possible solution to 2) that we have considered is using traci to reset each car's position and speed individually to the original state. This however, would not reset SUMO's time/step counters. Would increasing end time of the simulation to the maximum value (somewhere on the order of 10^15) have any unforeseen consequences as the numbers get very large? We're really excited to get working with SUMO and would love some guidance. Thank you! Cheers, Kanaad Parvate UC Berkeley EECS '19 ᐧ ------------------------------------------------------------------------------ Developer Access Program for Intel Xeon Phi Processors Access to Intel Xeon Phi processor-based developer platforms. With one year of Intel Parallel Studio XE. Training and support from Colfax. Order your platform today. http://sdm.link/xeonphi _______________________________________________ sumo-user mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/sumo-user
