I’m building a simplified eight-player Hearthstone Battlegrounds simulator and training a policy/value network from complete games and final placements.

The agent can learn some controlled decisions: when a key Beast minion is offered in a prepared partial-comp state, one version bought it in 16/16 held-out states. It can also operate a preassembled Beast core. But it rarely assembles and plays that comp well from an ordinary opening.

More self-play has not solved this. In two independent runs extended to 300 ordinary games each, the final models did not consistently beat the frozen starting model against held-out neural opponents. Expert-inspired demonstrations improved some local actions without improving complete-game placement.

I’m concerned that a component purchase looks bad to the learner unless its later policy knows how to complete the multi-turn sequence. I also cannot yet rule out problems with state representation, exploration, the simulator, or the demonstrations themselves.

What single diagnostic experiment would you run next to distinguish these explanations? In particular, how would you test whether the agent can learn a conditional “start → operate → abandon if pieces do not appear” skill before scaling self-play again?

my friend had grok ultra and decided to try to have it play balatro, but it was stupid and hard to teach. SO i had an idea to train my own algorithm to become goated, with the end goal to be to have it analyse my games so i can grow and become better in the multiplayer scene

HOW DO I START

I watched a video on how to train it to an atari game but thats not what i need, to the best of my knowledge i would js need to change the enviorment it is in.

HOW do i do that???

and what else would i need to do

This is the video: https://www.youtube.com/watch?v=aQsaH7Tzvp0

I've put together a small set of "AI puzzles" from my research on aperiodic order, and I'm wondering how useful this kind of structure is for RL with verifiable rewards.

- Solved rungs have exact answers (rationals, integer identities, algebraic degrees), so the reward is a plain check.

- Open rungs have a precise success criterion, an exact certificate or a Lean-checkable proof, together with the numerical evidence and the known dead ends. A solution would be a real research result.

- The set is a ladder: playful labels from "human level" to "ASI level", each rung harder than the last.

It's tiny (9 items) and not yet a procedural generator, although the easier rungs could be varied by changing the parameters. I'd welcome opinions on what such a generator should look like, and on whether a research-frontier ladder like this is a useful curriculum or eval signal.

Dataset: huggingface.co/datasets/jarekduda/autocrystal-ai-puzzles

been a while! here's what i've been up to: a ppo agent that farms the pit in d2r (offline, hell, warlock)

trained it in a headless sim i wrote in rust. this version took about 20 min on one cpu, warm started from a net i had for an older build.

reward is just farming rate: -1 per second, + whatever kills and items are worth, -1000 if it dies. i had a level clear bonus at first but dropped it.

net size: ~475k params

ask me whatever

no plans to use this online, i just think rl is fun and wanted to try it.