RL Environments
Interactive game environments purpose-built for reinforcement learning research. Agents can train, simulate, and test strategies in controlled settings with standardized rules, reward structures, and reproducible outcomes.
GameLab’s RL Environments are purpose-built interactive systems designed to train and evaluate agents in structured, game-based settings. Each environment encodes clear rules, state transitions, and reward mechanisms, enabling consistent experimentation across a wide range of decision-making scenarios, from deterministic puzzles to complex, imperfect-information games.
These environments are designed not as isolated experiments, but as part of a broader ecosystem. They integrate directly with human gameplay data, evaluation frameworks, and benchmarking systems, allowing researchers to move seamlessly from supervised learning to reinforcement learning to self-play. This creates a controlled but flexible foundation for studying how models learn, adapt, and optimize strategies over time.
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SFT (Supervised Fine-Tuning)
Human gameplay examples are used to fine-tune models through supervised learning, enabling models to learn strong baseline behaviors and replicate effective human decision-making patterns.
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Benchmarks
Standardized challenge suites built from real-world games that measure model performance over time. These benchmarks create consistent comparisons across systems in areas such as planning, imperfect information, and strategic decision-making.
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