[PATCHED] Download Cindy Car Simulator
Fernande Westmoreland
First, you can choose mega ramps from small to large according to your character level. This car ramp stunt game is very good like driving car jumps - from getting impossible tracks to mega ramps. Perform best car stunts, avoid obstacles and don't forget to enjoy realistic driving. Car crash simulator will let you enjoy 4k graphics and crash cars on purpose,
My daughter, Barb, moved back to Western Pennsylvania a few months ago. I assisted her in exploring this option last year. We stayed with her best friend, Cindy, who lives in Murrysville, just East of Pittsburgh. Cindy is married to a Lt Col in the Air National Guard at Pittsburgh International Airport, the 171st AIR REFUELING WING. During our visit, Chuck Perrott told me about the unit getting a new simulator for training pilots and keeping them current in the KC 135.
The 171st Air Refueling Wing has 16 KC 135 tankers and flies missions all over the world. They have 80 pilots attached to the Wing, and fly an impressive 10,000 hours per year in total with those airplanes. Chuck said that he could arrange some simulator flight time for me on my next visit to Pittsburgh.
My next visit was in May, 2018, and Chuck did arrange some time for me to "fly" the simulator on May 19. I asked if I could bring a friend. He said, "You are going to need a co-pilot, so sure, bring another pilot. My friend, Marty, flew from San Diego to Pittsburgh on May 18. On the 19th we checked into the ANG base about 10:15 and Chuck was waiting for us at the gate. He took us to the Operations Office.
After signing in, we had a sit down briefing about what to expect in the sim. It turns out that Cindy's brother, Bill, is a retired ANG pilot and now works at the base as an instructor in the simulator. It is possible to simulate any kind of weather and any flight emergency in this full motion simulator. I told Bill that neither of us are rated in an aircraft of this size, so please just basic flights will be fine. We moved into the simulator building and into the sim.
After formation flying we were running out of time as the simulator is scheduled for training almost all day, most days. Bill asked what I would like to do. I said that I wanted to do a landing on runway 28 L at Pittsburgh. He set us up for a long final, and by this time I was a bit more comfortable and made a good landing to a full stop.
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance of state-of-the-art offline and model-based RL methods degrade significantly given such limited data availability, even for commonly perceived "solved" benchmark settings such as "MountainCar" and "CartPole". To address this challenge, we propose PerSim, a model-based offline RL approach which first learns a personalized simulator for each agent by collectively using the historical trajectories across all agents, prior to learning a policy. We do so by positing that the transition dynamics across agents can be represented as a latent function of latent factors associated with agents, states, and actions; subsequently, we theoretically establish that this function is well-approximated by a "low-rank" decomposition of separable agent, state, and action latent functions. This representation suggests a simple, regularized neural network architecture to effectively learn the transition dynamics per agent, even with scarce, offline data. We perform extensive experiments across several benchmark environments and RL methods. The consistent improvement of our approach, measured in terms of both state dynamics prediction and eventual reward, confirms the efficacy of our framework in leveraging limited historical data to simultaneously learn personalized policies across agents.
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