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Comprehensive TensorBoard-Visualisierung Tools for Every Need

Get access to TensorBoard-Visualisierung solutions that address multiple requirements. One-stop resources for streamlined workflows.

TensorBoard-Visualisierung

  • mini-AlphaStar

    Simplified PyTorch implementation of AlphaStar, enabling StarCraft II RL agent training with modular network architecture and self-play.
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    What is mini-AlphaStar?
    mini-AlphaStar demystifies the complex AlphaStar architecture by offering an accessible, open-source PyTorch framework for StarCraft II AI development. It features spatial feature encoders for screen and minimap inputs, non-spatial feature processing, LSTM memory modules, and separate policy and value networks for action selection and state evaluation. Using imitation learning to bootstrap and reinforcement learning with self-play for fine-tuning, it supports environment wrappers compatible with StarCraft II via pysc2, logging through TensorBoard, and configurable hyperparameters. Researchers and students can generate datasets from human gameplay, train models on custom scenarios, evaluate agent performance, and visualize learning curves. The modular codebase enables easy experimentation with network variants, training schedules, and multi-agent setups. Designed for education and prototyping rather than production deployment.

  • 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.

  • 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.
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