As far as I can tell, the main goal for reinforcement learning is to make it so that it doesn't take 10k learning sessions to learn what a human can learn in a single session, and to make self-training without guiding scenarios feasible.
"The Football Engine is written in highly optimized C++ code, allowing it to be run on off-the-shelf machines, both with GPU and without GPU-based rendering enabled. This allows it to reach a performance of approximately 25 million steps per day on a single hexa-core machine."
Also has some features geared towards generalization, and benchmarking:
"With the Football Benchmarks, we propose a set of benchmark problems for RL research based on the Football Engine. The goal in these benchmarks is to play a “standard” game of football against a fixed rule-based opponent that was hand-engineered for this purpose. We provide three versions: the Football Easy Benchmark, the Football Medium Benchmark, and the Football Hard Benchmark, which only differ in the strength of the opponent. "
"As training agents for the full Football Benchmarks can be challenging, we also provide Football Academy, a diverse set of scenarios of varying difficulty. This allows researchers to get the ball rolling on new research ideas, allows testing of high-level concepts (such as passing), and provides a foundation to investigate curriculum learning research ideas, where agents learn from progressively harder scenarios."
So, as compared to SC2/Dota, you can train much faster, with good baselines to benchmark or compete again, and already built-in support for curriculum learning. SC2/Dota weren't designed for RL, so they're just harder to work with in RL - SC2 has a curriculum which was added on afterwards, for example (the minigames), but Dota2 still doesn't.
Soccer is also more popular than either SC2/DoTA2 as well, so that may be a draw for researchers. More interesting to work on something you like and already know about.
Dunno if all of that together is enough to make it a worthwhile testbed, but it's not obviously worthless or redundant.
That is 24 steps per second per thread (or 48 per core).
This doesn't seem that impressive: much more complex games run at that frame rate? FIFA games from the 90s don't look much worse and certainly achieved those frame rates on much older hardware.
That's not the point. Yes it superficially appears to be a simpler and less polished game than SC2/Dota, but the point is that it's a different kind of learning environment. It has different actions (things the AI can do, how it controls player movements), different observations (what the AI perceives/sees is structured differently), different rules/physics (this has a ball you can run to and kick).
In other words, you can perform different kinds of experiments and learn different things by studying this environment. The kind of AI that would excel at this game could have a different architecture and be trained differently than what you need to be effective at SC2/Dota. Just as you would learn different things designing a Quake bot using deep learning (your Quake bot would need to learn to navigate and map out 3D space, for one).
For starters, it doesn't require you to buy it and it won't bring Activision trademark lawyers with copyright claims down on your YouTube channel if you record it.
It looks like the AI agent can control one or all of the players on their team.
> by default, our non-active players are also controlled by another rule-based bot. In this case, the behavior is simple and corresponds to reasonable football actions and strategies, such as running towards the ball when we are not in possession, or move forward together with our active player. In particular, this type of behavior can be turned off for future research on cooperative multi-agents if desired.
Missed opportunity to use Rust for memory safety.
Blind language allegiances are useless. All of them have their advantages and it makes no sense to claim one is superior.
Interesting to see how things like faster players change optimal play
