ppo-Pyramids is an open-source reinforcement learning agent model designed for the Pyramids environment using ML-Agents. Below are 7 other ai apps with similar functionality to Ppo Pyramids, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
Crazylazylife/PyramidsRND is an open-source reinforcement learning model tailored for use with ML-Agents environments. It supports training, evaluation, and visualization with tools like TensorBoard and ONNX, making it suitable for AI researchers and developers in reinforcement learning.
ppo-Huggy is an open-source reinforcement learning model designed for use with ML-Agents and robotics simulations. It provides pretrained weights and integration instructions for developers working on AI and robotics projects. The model supports ONNX export and TensorBoard visualization.
ppo-Huggy is an open-source reinforcement learning agent checkpoint compatible with ML-Agents, designed for deep RL research and experimentation. It enables researchers and developers to run local inference, resume training, and evaluate agent performance in custom environments.
ppo-Huggy is an open-source reinforcement learning agent model compatible with Unity ML-Agents. It enables researchers and developers to train, evaluate, and deploy agents in simulated environments, supporting training resumption and agent evaluation workflows.
ppo-Huggy is an open-source reinforcement learning model designed for use with ML-Agents environments. It supports integration with ML-Agents, ONNX export, and TensorBoard visualization, making it suitable for researchers and developers working on RL projects.
ppo-LunarLander-v3 is an open-source reinforcement learning agent trained for the LunarLander-v3 environment. It is designed for AI researchers and developers working with RL algorithms and supports integration with stable-baselines3 and CLI tools.
ppo-SnowballTarget is an open-source reinforcement learning agent model trained for the SnowballTarget environment. It is intended for RL researchers and developers using ML-Agents and ONNX for experimentation and benchmarking.
