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Ultimate experimentação em IA Solutions for Everyone

Discover all-in-one experimentação em IA tools that adapt to your needs. Reach new heights of productivity with ease.

experimentação em IA

  • Vanilla Agents

    Vanilla Agents provides ready-to-use implementations of DQN, PPO, and A2C RL agents with customizable training pipelines.
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    What is Vanilla Agents?
    Vanilla Agents is a lightweight PyTorch-based framework that delivers modular and extensible implementations of core reinforcement learning agents. It supports algorithms like DQN, Double DQN, PPO, and A2C, with pluggable environment wrappers compatible with OpenAI Gym. Users can configure hyperparameters, log training metrics, save checkpoints, and visualize learning curves. The codebase is organized for clarity, making it ideal for research prototyping, educational use, and benchmarking new ideas in RL.

  • CrewAI-Learning

    CrewAI-Learning enables collaborative multi-agent reinforcement learning with customizable environments and built-in training utilities.
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    What is CrewAI-Learning?
    CrewAI-Learning is an open-source library designed to streamline multi-agent reinforcement learning projects. It offers environment scaffolding, modular agent definitions, customizable reward functions, and a suite of built-in algorithms such as DQN, PPO, and A3C adapted for collaborative tasks. Users can define scenarios, manage training loops, log metrics, and visualize results. The framework supports dynamic configuration of agent teams and reward sharing strategies, making it easy to prototype, evaluate, and optimize cooperative AI solutions across various domains.

  • Reinforcement Learning Agents for PettingZoo Games

    A GitHub repo providing DQN, PPO, and A2C agents for training multi-agent reinforcement learning in PettingZoo games.
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    What is Reinforcement Learning Agents for PettingZoo Games?
    Reinforcement Learning Agents for PettingZoo Games is a Python-based code library delivering off-the-shelf DQN, PPO, and A2C algorithms for multi-agent reinforcement learning on PettingZoo environments. It features standardized training and evaluation scripts, configurable hyperparameters, integrated TensorBoard logging, and support for both competitive and cooperative games. Researchers and developers can clone the repo, adjust environment and algorithm parameters, run training sessions, and visualize metrics to benchmark and iterate quickly on their multi-agent RL experiments.

  • LLM Clash

    Have your LLM debate other LLMs in real-time.
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    What is LLM Clash?
    LLM Clash is a dynamic platform designed for AI enthusiasts, researchers, and hobbyists who want to challenge their large language models (LLMs) in real-time debates against other LLMs. The platform is versatile, supporting both fine-tuned and out-of-the-box models, whether they are locally hosted or cloud-based. This makes it an ideal environment for testing and improving the performance and argumentative abilities of your LLMs. Sometimes, a well-crafted prompt is all you need to tip the scales in a debate!
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