Machina is a PyTorch-based Deep Reinforcement Learning framework designed for real-world applications, emphasizing high composability and flexibility. It targets researchers and practitioners needing to integrate diverse RL algorithms, handle mixed simulated and real-world environments, or dynamically adjust hyperparameters. Its core benefit is simplifying complex RL setups through an episode-based trajectory interaction model.

How It Works

Machina's design centers on "composability," allowing components like environments or algorithms to be swapped dynamically during execution. This is achieved by abstracting interactions through generated trajectories, making it easy to switch between, for instance, a simulated and a real-world environment or combine on-policy and off-policy algorithms. This approach facilitates complex configurations like meta-learning or hybrid algorithm implementations that are challenging in other libraries.

Quick Start & Requirements

• Install: pip install machina-rl or from source.
• Prerequisites: Python 3.5-3.7, PyTorch 1.0.0+.
• Resources: Quickstart and examples are available.

Highlighted Details

• Supports mixed on-policy/off-policy algorithm combinations (e.g., PPO + SAC).
• Facilitates meta-RL by easily sampling from multiple environments.
• Implemented algorithms include PPO, TRPO, SAC, DDPG, Behavior Cloning, and more.
• Designed for episode-based sampling, suitable for real-world scenarios.

Maintenance & Community

• Official web page and documentation are available.

Licensing & Compatibility

• License not specified in the README.

Limitations & Caveats

• The README does not specify the license, which is critical for commercial use.
• Algorithms requiring step-by-step updates (e.g., DQN, DDPG) are not reproduced.