ppo-Huggy is an open-source reinforcement learning model designed for use with ML-Agents environments. Below are 6 other ai apps with similar functionality to Ppo Huggy, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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 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 model designed for use with ML-Agents. It supports training, evaluation, and deployment of RL agents, and is available via Hugging Face for developers and researchers in AI and machine learning.
ppo-Pyramids is an open-source reinforcement learning agent model designed for the Pyramids environment using ML-Agents. It is intended for researchers and engineers working on deep reinforcement learning and simulation-based AI projects.
HAPPO HATRPO is a reinforcement learning resource focused on scalable deployment in multi-agent environments. The project provides trained model checkpoints and code for research involving the Inter-Class Actor-Critic approach, enabling changes to the agent count at deployment without requiring retraining. It is designed to support research snapshots and experimentation with scalable multi-agent reinforcement learning. The available files include actor and critic models for each agent class, specifically using a count-invariant graph neural network (GNN) with attention mechanisms for the actor, and a GNN class-factored central critic for training. Optimizer states are also provided to facilitate resuming training, along with configuration metadata detailing aspects such as map, classes, and hidden size. The tool targets scenarios involving the StarCraft Multi-Agent Challenge (SMAC), with provided checkpoints for maps like 2s3z and 3s5z, which feature different team sizes but the same unit classes. Additionally, results files such as zero-shot transfer win rates are included, supporting research into scaling and transferability in multi-agent systems. HAPPO HATRPO is implemented using PyTorch and supports integration with TensorBoard for monitoring training metrics. The project is released under the MIT License, allowing open access and modification. It is delivered through downloadable files and code, with further resources accessible via its GitHub repository. The tool is intended for researchers and practitioners working in multi-agent reinforcement learning, particularly those interested in scalable deployment and experimentation with agent class variations in environments like StarCraft. No information is provided about commercial pricing or proprietary licensing, as the evidence indicates the use of the MIT License. The project is positioned within the class of reinforcement learning tools, with a particular emphasis on scalable, multi-agent deployments and research reproducibility.
