{"repo":"sidmohan0/tesserack","free":true,"listed":false,"github":"https://github.com/sidmohan0/tesserack","clone":"git clone https://github.com/sidmohan0/tesserack.git","description":"Compiling strategy guides into reward functions for reinforcement learning. Uses Claude Vision to extract unit tests from game guides, then trains agents with dense, interpretable rewards.","language":"JavaScript","stars":38,"topics":["browser-game","gameboy","machine-learning","neural-network","pokemon","reinforcement-learning","tensorflow-js","webgpu","claude","llms"],"license":null,"category":"machine-learning","readme_excerpt":"Tesserack Compiling strategy guides into reward functions for reinforcement learning. What is this? Most RL game agents learn from scratch with sparse rewards (\"you won\" / \"you lost\"). Tesserack takes a different approach: it uses an LLM to read a strategy guide and extract structured \"unit tests\" that fire as dense rewards throughout gameplay. The strategy guide becomes a curriculum. Instead of stumbling randomly until the agent accidentally beats Brock, it gets rewarded for: - Walking toward the gym (+0.1) - Entering the gym door (+2.0) - Winning the badge (+50.0) How it works 1. Extract : Claude Vision reads pages from the Prima Strategy Guide and extracts locations, objectives, and map coordinates 2. Compile : Extractions become tiered unit tests (movement → landmarks → objectives) 3. Train : REINFORCE policy network gets dense rewards as tests fire The LLM acts as a \"compiler\" that translates human-readable instructions into machine-executable reward signals. Reward Tiers Tier What It Rewards Example Reward ------ ----------------- --------- -------- Tier 1 Micro movement Coordinates changed, moved toward objective 0.1 - 0.2 Tier 2 Landmarks Reached Oak's Lab region, entered a door 2.0 - 5.0 Tier 3 Objectives Got starter Pokemon, earned badge 10.0 - 50.0 Penalties Bad behavior Stuck for 30+ frames -0.5 Inspired by OLMoCR-2 OLMoCR-2 showed that unit tests make excellent reward signals - deterministic, interpretable, and dense. Tesserack applies that insight to game playin","default_branch":null,"files":null,"tree":[],"storefront":"/r/sidmohan0","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sidmohan0/tesserack/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}