biological-alignment-benchmarks/biological-alignment-gridagents-benchmarks

Safety challenges for RL and LLM agents' ability to learn and use biologically and economically aligned utility functions. The benchmarks are implemented in a gridworld-based environment. The environments are relatively simple, just as much complexity is added as is necessary to illustrate the relevant safety and performance aspects.

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Emerging

Implements eleven gridworld benchmarks across three developmental stages—basic biologically-inspired dynamics, multi-objective learning, and multi-agent cooperation—addressing overlooked safety aspects like homeostasis, diminishing returns, and resource sharing. Built on the Extended Gridworlds framework, it provides training implementations for Stable Baselines 3 RL agents and LLM-based agents, with extensible templates for custom agent development. Designed for empirical evaluation of how agents learn utility functions aligned with biological and economic principles rather than pure reward maximization.

No Package No Dependents
Maintenance 10 / 25
Adoption 4 / 25
Maturity 9 / 25
Community 15 / 25

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Stars

7

Forks

5

Language

Python

License

MPL-2.0

Last pushed

Mar 06, 2026

Commits (30d)

0

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