PPO
6 stories filed under PPO on Beyond Market Intelligence. The newest of them: “Watch an AI learn Clash Royale defense in a live browser demo”, “Teaching an AI to Game the System: How a Cannon Found the Perfect Loophole”, and “Finding the Right Settings for Multi-Agent PPO Training”. Watching a policy learn is different from reading about it. The agent found the loophole because the rules allowed it. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every PPO story on Beyond Market Intelligence, newest first.
Watch an AI learn Clash Royale defense in a live browser demo
Watching a policy learn is different from reading about it. This live browser demo makes that visible: a 5,629-parameter agent trains with REINFORCE in plain JavaScript, while every rollout runs through the C++ engine compiled to WebAssembly. The chart shows the brute-forced optimum, so the gap between learned behavior and best answer is always in view. One honest detail stands out: Giant vs Cannon traps most runs in a lane placement worth 75% of optimal.
Teaching an AI to Game the System: How a Cannon Found the Perfect Loophole
The agent found the loophole because the rules allowed it. By parking its Cannon behind its own King, it turned a costly fight into a free decay, gaming the simulation's blind spot. That is the kind of insight that only surfaces when you build the engine yourself, as this team did with a deterministic C++ core and cheap lookahead. The win-rate jump from 0.625 to 0.944 is impressive, but the real lesson is structural: how you define cost shapes what the agent exploits.
Finding the Right Settings for Multi-Agent PPO Training
Hyperparameter tuning for multi-agent reinforcement learning is rarely a one-size-fits-all affair. When you're comparing PPO variants across VMAS tasks, the fact that optimal settings drift between architectures and scenarios is expected, not a flaw. Methodologically, unifying hyperparameters is the only way to isolate architectural value, but note two: forced uniformity can break convergence. That tension is real. Since your goal is testing robustness under frozen-model adversarial attacks, you are effectively evaluating resilience, not raw performance.
Explore how DelveRL opens new paths for training game-playing agents.
Most roguelikes punish agents with messy integration, but DelveRL flips that script. SnyderConsulting built an open-source, turn-based game with a structured API, deterministic simulation, and procedural floors, designed specifically for training game-playing agents. It runs locally, includes a recurrent PPO baseline that hits floor 18 on average, and everything is open source. That's a practical gift to the research community. The strategic depth looks promising, and the transparent benchmarks make it easy to test new ideas.
Teaching PPO to Track the Ball Instead of Memorizing Scores
After 124 failed PPO experiments on Atari Breakout, Mikey Harrell found something worth celebrating: every model defaulted to a memorized script. The fix wasn't more environment engineering. It was three lines of reward shaping, a tiny proximity bonus that rewards tracking the ball during descent. The behavior transfers to clean evaluation, and his Split-Watcher tool lets you see it live. It's a smart, human-centered insight into why optimization pressure matters more than complexity.
Discover how AI masters Snake in under ten hours of training
Training an AI to hit 86 points in Snake, just shy of the 87-point ceiling, after under 10 hours on a free Colab GPU is no small feat. It shows real intent. Running 4,096 games in parallel with a CoordConv architecture that preserves the spatial grid is a smart approach. The focus on GPU-native efficiency is where the future of this work lives. For next steps, I would look at reward shaping or curiosity-driven exploration.