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benchmarking IA

  • LifelongAgentBench

    A benchmarking framework to evaluate AI agents' continuous learning capabilities across diverse tasks with memory, adaptation modules.
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    What is LifelongAgentBench?
    LifelongAgentBench is designed to simulate real-world continuous learning environments, enabling developers to test AI agents across a sequence of evolving tasks. The framework offers a plug-and-play API to define new scenarios, load datasets, and configure memory management policies. Built-in evaluation modules compute metrics like forward transfer, backward transfer, forgetting rate, and cumulative performance. Users can deploy baseline implementations or integrate proprietary agents, facilitating direct comparison under identical settings. Results are exported as standardized reports, featuring interactive plots and tables. The modular architecture supports extensions with custom dataloaders, metrics, and visualization plugins, ensuring researchers and engineers can adapt the platform to varied application domains.
    LifelongAgentBench Core Features
    • Multi-task continuous learning scenarios
    • Standardized evaluation metrics (adaptation, forgetting, transfer)
    • Baseline algorithm implementations
    • Custom scenario API
    • Interactive result visualization
    • Extensible modular design
    LifelongAgentBench Pro & Cons

    The Cons

    No information on direct commercial pricing or user support options.
    Limited to benchmarking and evaluation, not a standalone AI product or service.
    May require technical expertise to implement and interpret evaluation results.

    The Pros

    First unified benchmark specifically focused on lifelong learning in LLM agents.
    Supports evaluation across three realistic interactive environments with diverse skill sets.
    Introduces a novel group self-consistency mechanism to enhance lifelong learning efficiency.
    Provides task dependency and label verifiability ensuring rigorous and reproducible evaluation.
    Modular and comprehensive task suite suitable for assessing knowledge accumulation and transfer.

  • Multi-Agent DDPG with PyTorch & Unity ML-Agents

    Implements decentralized multi-agent DDPG reinforcement learning using PyTorch and Unity ML-Agents for collaborative agent training.
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    What is Multi-Agent DDPG with PyTorch & Unity ML-Agents?
    This open-source project delivers a complete multi-agent reinforcement learning framework built on PyTorch and Unity ML-Agents. It offers decentralized DDPG algorithms, environment wrappers, and training scripts. Users can configure agent policies, critic networks, replay buffers, and parallel training workers. Logging hooks allow TensorBoard monitoring, while modular code supports custom reward functions and environment parameters. The repository includes sample Unity scenes demonstrating collaborative navigation tasks, making it ideal for extending and benchmarking multi-agent scenarios in simulation.
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