Browser demo of our Clash Royale RL environment: a 5.6k-parameter REINFORCE policy learns defensive placement against a brute-force optimum [P]

Excerpt

Yesterday I shared our open-source Clash Royale simulator and its recurrent PPO agent here. A training loop is easier to understand when you can watch it, so we put a small interactive version online: https://itzik123.github.io/ClashRoyaleAi/lab/ The task is one decision. An attacker spawns at a random point on the enemy side, and the policy picks a legal cell for one defending card, then a delay of 0 to 5 s given that cell. The reward is the fraction of tower damage prevented relative to no def