Play-gent

Our agent uses OpenEnv to build a curriculum of video game environments that train a TinyLlama 1.1B agent via GRPO reinforcement learning. We implement three OpenEnv-compliant environments — Diplomacy (coalition tactics), webDiplomacy human gameplay (211k real states), and IRC poker (bluff detection) — each with dense reward signals that teach the core primitives of strategic negotiation. The agent learns through RL across this curriculum: Phase 1 trains coalition pressure in Diplomacy, Phase 2 grounds it in human gameplay patterns, Phase 3 unifies all three reward signals in a live arbitrage environment. The result is an agent that transfers video game negotiation skills to real economic interactions — starting with $20, it compounds capital to $80 (4x return) against Groq Llama 3.1 8B adversarial sellers, detecting bluffs at 97% confidence and reaching actual price floors 100% of the time.