Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no unified benchmark enables fair comparison across these approaches. We present Agentick, a benchmark for sequential decision-making agents designed to evaluate RL, LLM, VLM, hybrid, and human agents on common ground and to power research on the fundamental challenges of sequential decision-making. Agentick provides 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities, all exposed through a single Gymnasium-compatible interface. The benchmark ships with a Coding API, oracle ...
| Authors | Roger Creus Castanyer, Pablo Samuel Castro, Glen Berseth |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 25 September 2026 |
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