Re: Robocode and reinforcement learning

Hi Pavel - 

I've added answers below.

On Saturday, March 13, 2021 at 3:49:03 PM UTC-5 Pavel Savara wrote:

>
> Do I see it well that it took 3000 rounds to learn to lock the gun on the 
> enemy ?
>

It takes about 5 rounds for the agent to settle into a decent aiming 
policy. The graph on the right shows time steps ("turns" in Robocode) for a 
single round. I've updated the webpage 
<https://matthewgerber.github.io/rlai/case_studies/robocode.html> with 
additional details about this. Now you can see separate videos for training 
and testing. The training rounds tend to be chaotic because the learning 
agent is mixing its aiming policy with random behavior to maintain 
exploration and learning. In the testing video, the agent has no random 
behavior, so it's clear what the policy is:  rotate radar --> obtain 
bearing --> rotate gun --> fire. It was neat to see this tactic emerge from 
the specified features.

How much of that is done by you in feature selection ?
>

The updated webpage explains the features used. It looks like 5 features is 
sufficient to do radar-driven aiming against a stationary opponent.
 

> What's coming next ?
>

I also added a couple TBD sections:  aiming against a mobile robot, and 
evasive movement.

Are you interested in working on this, or know anyone who might be? This is 
just a personal hobby project of mine, but I'd welcome collaboration.

Matt

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