CoreData Engine

RL Environment

Structured simulation environments and scenario datasets that let reinforcement learning agents train on realistic, high-fidelity data.

One RL episode, labelled An example reinforcement-learning episode on an icy-merge edge case: six states, the action taken at each, and the reward for each step, including a negative reward for accelerating on ice that an expert corrected, and a sparse reward of +1 at the goal. The trajectory is ready for offline RL as state, action, reward and done. One RL episode, labelled States, actions and the reward behind each step Coming soon scenario: icy merge · edge case reward +0.1 s0 cruise +0.1 s1 signal -0.6 s2 accelerate +0.3 s3 ease off +0.4 s4 merge +1.0 s5 settle ← reward corrected by expert READY FOR OFFLINE RL State · action · reward · done sparse goal reward + dense shaping human-validated reward labels

RL Environment Services — Coming Soon

We are developing curated reinforcement learning environment datasets — annotated state-action trajectories, reward signal labeling, and scenario construction for real-world RL applications.

From robotics and autonomous systems to conversational agents and game AI, our RL environment data services will provide the grounded, expert-labeled training scenarios your agents need.

Trajectory Annotation

Expert-labeled state-action-reward sequences for imitation learning and offline RL.

Scenario Construction

Custom environment design and edge-case scenario generation for robust agent training.

Reward Signal Labeling

Human-validated reward functions and sparse/dense reward annotations across domains.

Domain Simulation Data

Robotics, autonomous driving, logistics, and game environments with rich metadata.

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