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@biological-alignment-benchmarks

Biological Alignment Benchmarks

Safety challenges for RL and LLM agents' ability to learn and act in desired ways in relation to biologically and economically relevant aspects.

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  1. ai-safety-gridworlds ai-safety-gridworlds Public

    Forked from google-deepmind/ai-safety-gridworlds

    Extended, multi-agent and multi-objective (MaMoRL / MoMaRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety…

    Python 11 2

  2. biological-alignment-gridworlds-benchmarks biological-alignment-gridworlds-benchmarks Public

    Safety challenges for AI agents' ability to learn and act in desired ways in relation to biologically and economically relevant aspects. The benchmarks are implemented in a gridworld-based environm…

    Python 5 2

  3. bioblue bioblue Public

    Notable runaway-optimiser-like LLM failure modes on Biologically and Economically aligned AI safety benchmarks for LLM-s with simplified observation format. The benchmark themes include multi-objec…

    Python 3 2

  4. zoo_to_gym_multiagent_adapter zoo_to_gym_multiagent_adapter Public

    Enables you to convert a PettingZoo environment to a Gym environment while supporting multiple agents (MARL). Gym's default setup doesn't easily support multi-agent environments, but this wrapper r…

    Python 1

Repositories

Showing 4 of 4 repositories
  • ai-safety-gridworlds Public Forked from google-deepmind/ai-safety-gridworlds

    Extended, multi-agent and multi-objective (MaMoRL / MoMaRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.

    biological-alignment-benchmarks/ai-safety-gridworlds’s past year of commit activity
    Python 11 Apache-2.0 128 0 0 Updated Nov 7, 2025
  • bioblue Public

    Notable runaway-optimiser-like LLM failure modes on Biologically and Economically aligned AI safety benchmarks for LLM-s with simplified observation format. The benchmark themes include multi-objective homeostasis, (multi-objective) diminishing returns, complementary goods, sustainability, multi-agent resource sharing.

    biological-alignment-benchmarks/bioblue’s past year of commit activity
    Python 3 MPL-2.0 2 0 0 Updated Nov 7, 2025
  • biological-alignment-gridworlds-benchmarks Public

    Safety challenges for AI agents' ability to learn and act in desired ways in relation to biologically and economically relevant aspects. 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.

    biological-alignment-benchmarks/biological-alignment-gridworlds-benchmarks’s past year of commit activity
    Python 5 MPL-2.0 2 0 0 Updated Nov 7, 2025
  • zoo_to_gym_multiagent_adapter Public

    Enables you to convert a PettingZoo environment to a Gym environment while supporting multiple agents (MARL). Gym's default setup doesn't easily support multi-agent environments, but this wrapper resolves that by running each agent in its own process and sharing the environment across those processes.

    biological-alignment-benchmarks/zoo_to_gym_multiagent_adapter’s past year of commit activity
    Python 1 MPL-2.0 0 0 0 Updated Aug 23, 2025

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