Grand Theft Auto V

By using a variety of console commands you can add some really cool effects to the game, including invincibility and moon-style gravity. The best thing about its list of cheats is that they can be used to transform the game completely — the aforementioned moon gravity being a fine case in point — or just to take the edge off those tougher missions by replenishing your health, armour, and ammo. You know — just for a treat.
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Contact What’s Psyber up to? The most recent project is self-driving vehicles in Grand Theft Auto V. The environment of Grand Theft Auto 5 was chosen specifically for it’s challenging atmosphere.

Road conditions change, other drivers behave uniquely, and even ambient time and weather changes much like the real world. I should stress: Rather than neural network output being reserved for single actions, and those actions being full or nothing, Charles now tweaks inputs according to the network’s output for each action.

Adding a visual speedometer to the game gives the AI the opportunity to learn how fast it is going. Judging from performance, it does appear that the AI has learned a few things from the speedometer. Model This is the same model back from 0.

Training took about 1 week. Results The video above comes from 0. This is a good example of the AI being able to, in a much more controlled manner, weave in and out of traffic.

Prior to this model, the AI would have taken max left and max right turns, and crashing would be much more likely. Above is another example of quite a few instances where fine-steering got us through a cluster of scenarios where full turning would have had us crashing.

Along with more finely-tuned turning, Charles can now control his throttle granularly rather than having no throttle or pedal to the metal. An example of both throttle and steering control saving us: Now bringing in the speedometer with v0. Charles seems to handle turns even better with the additional information. Charles is far too dependent on the waypoints to drive. Most of the time, when entering a tunnel and losing the waypoints on the map GTA V simulates “losing signal” on the map , Charles tends to get stuck in the tunnel.

That said, he can sometimes be successful, for example: Along with the above point, Charles often still runs directly into vehicles in front of him, giving me the impression he’s focused too much on the map.

That said, he does avoid cars, along with doing things like modulating speed in most vehicles, keeping the speedometer dial to about 90 degrees in many cases. Here’s an example of Charles in a Ramp Buggy a car that is just basically a ramp, so hitting other cars just sends them flying v0. Up until this point, the AI was trained to, in general, stay on the road and avoid obstacles, but never actually had a set objective.

If the AI got into a collision, or bumped something, and the course was changed, then the AI would just simply look for a road again and continue a random journey. Objectives The main objective here Hopefully continue to see improvements in the driving of the agent, test the inclusion of following waypoints in the training data, and increase input resolution to the network to x At a resolution of x90, it’s pretty challenging to read the game’s map, and the intention is that that AI will still only use visual elements to make decisions.

Here’s an image comparing the game resolutions: At the beginning things looked alright: Various training steps along the way, colors are just due to different training times: Unfortunately it continued to taper: In terms of time: Of course, I knew the Inception model is totally capable of working with this data, it’s been successful in the past, the only change here is the resolution. At this point, I decide to begin manipulating Inception V3, here’s my modified version: Training Training Data: I suspect this is due to the nature of this task where, when shuffling data, the “test” frames, despite being unique, are still very similar to training frames.

There we go, much better. Now, I could have continued training this model, but I was pretty darn tired of training models, so I went ahead and pushed v0. I will also note that the training above was done with a decaying learning rate. I started with 0. Another major impact to training that I found besides decreasing the learning rate was the batch size. Initially, I was using batches of just samples, since this translated to MB of data.

I then rose this to batches of 2K files, and noticed some improvement. I upped it to 4K, more improvement, so then finally I increased batch sizes to 10K samples, which was about GB of data per batch. I could probably continue to increase this, but, for now, this seems to be a good number. Results Armed with the confidence that the model was at least capable of seeing the waypoints and paths on the game map, I went back and began making more waypoint-following data, and then trained the model again.

Somewhat comically, it’s clear the AI may have too much focus on the map, rather than the game world, but we can certainly work on this. Even when missing the objective, Charles, our agent, will often try to get back on track, and is often successful: U-Turns appear to be the most challenging task for Charles, but he is slowly learning them: Missing objectives frequently has Charles trying to drive through walls to get back on track.

A lot of crashing still occurs, but Charles has significantly improved his handling of the vehicle. I’d still like to see this improved. Charles performs VERY well at night, and actually much worse in the day. This used to be the opposite, so obviously I’d like to bring day-time performance up to where night performance is now.

Conclusion Charles still continues to exceed my expectations on what’s possible with an AI on a frame-by-frame basis with no memory, and now he even has a purpose as he drives. I would have thought even for an AI like this, as bad as Charles is, to still require some form of short term memory. With waypoints, I have begun tracking Charles’ time between objectives, and this data can be used to bring in reinforcement learning. That said, I don’t believe that is actually the correct path forward at this stage.

Eventually, I would make sense, but I suspect that, while running a single instance, reinforcement learning would still take years upon years to see decent results, at least using methods that I can conjur up. I will continue thinking on this, but I suspect Charles will need much more human assistance still moving forward. Future Considerations: Memory in some form is still important, but I have yet to find something that outperforms the base CNN.

Reinforcement learning, though of course the same rule for memory applies here. Continued focus on waypoints – I think this is the best path forward from this point, and is going to be where I continue to put my energies Granular control is another area of interest, but, like memory and reinforcement learning, I have yet to find a solution that performs better than the keyboard model.

This is probably since the model is trained this way, but I will keep working on this as well. With color, minimal model changes should need to be made, but we will have to see how it plays out. The only change here is, with the input layer, we now have 3 channels for R,G and B color values. It is expected that color will make feature creation and detection much easier for the network, resulting in more robust features as well as reducing mistakes in practice.

It is also expected that color is going to make learning to drive in other scenarios like night, rain and fog more realistic. I opted to also explore Google’s Inception V3. Objectives Be able to drive with similar or better performance than v0. Another failsafe used motion detection to determine if the vehicle was stuck, and it would try to wiggle out of wherever it was, but there are many ways to get permanently stuck in GTA V.

Model For this next version, I trained two neural network models: I brought height back to 90 to have the more common The Inception model used here: Expanded AlexNet trained in under 2 days, Inception took 4 days.

Both AlexNet and Inception v3 were able to fit the data with no clear overfitment issues. Results This is where things get interesting. Inception certainly took much longer to train, but the actual training statistics of both models made them look very similar. For this project, I have two machines that I have been using. For my purposes, I call the training machine my “main machine” and then I call the testing machine, the one that runs the stream, the “Charles” machine, since I have named my driving Agent “Charles.

I tested them both in the clear day, in the rain, with police, off road, in fog Upon putting the expanded AlexNet model on the Charles machine, it was clear something was wrong.

The Agent Certainly worse than v0. Curious, I stopped the expanded AlexNet model and loaded in the Inception v3 model. To my surprise, the Inception model performed much better, and actually drove well in all conditions.

I went back and compared all graphics settings between games, and I could not find any differences, besides one: Changing this appeared to help night time driving a bit, but there were still major differences in quality between machines, despite both machines running almost identical hardware. While this is just one example, it seems to suggest to me that the inception model is more robust to subtle differences, and that the AlexNet model I was using was far too sensitive to tiny changes.

I plan to continue to look into this interesting finding. Conclusion Color has indeed allowed the models to drive in more conditions, but it looks like the expanded alexnet was far too finely-tuned for some sort of difference that is not noticeable to me. I still have no idea why it worked fine on the main machine but not on the Charles machine.

I still see this version as a success, and I am still amazed that it works at all, still only making decisions one frame at a time with no memory of what it’s been doing or what previous frames were. Improved resolution – x is pretty challenging for really seeing things, especially at distances that we need to see them if we’re to be driving at speed.

Memory – Intuitively, this seems to be likely a requirement for near-flawless driving. Reinforcement Learning More Data Granular control – I actually implemented emulating an xbox controller in attempt to get more finely tuned controls, but couldn’t get anything to perform better than pure key presses.

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