Environments
The space to test and transform.
We offer a wide array of training environments - our own spaces where AI can safely train and transform its capability. From strategy to spatial to reasoning up to reinforcement learning with human feedback.
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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Benchmarking
Our benchmarking goes deeper than specialized tasks and surface intelligence. We’ve created a new Cognitive Index Score to measure AI capabilities across an array of eight brain functions.
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RL & Training
Open models, Vision Language Models, Language Models or World Models - our data is multimodal and our training environments have been crafted to support them all. Training with Gamelab will improve your model, whether specialized or general, in areas you wouldn’t have expected.
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