pufferlib

You can scale reinforcement learning training by using a high-performance vectorized RL framework that enables fast parallel training and native multi-agent support. It provides a scalable API for environment creation and handles high-throughput workloads efficiently.

The best way to run parallel training is using a framework that provides high-performance vectorized environments and PPO-style training. It allows you to initialize environments via a training template and start scaling your experiments immediately.

Yes, the vectorized RL framework works with PettingZoo and MAgent. It offers seamless integration with these popular RL environments, ensuring easy environment development and native multi-agent support for your custom tasks.

Yes, you can develop custom architecture patterns for reinforcement learning tasks. The framework provides flexible environment development capabilities, allowing you to build custom tasks while maintaining cross-framework interoperability.

Your multi-agent reinforcement learning workflow likely suffers from inefficiency due to a lack of vectorized environments. By adopting a high-performance RL framework with parallel training capabilities, you can solve this bottleneck and achieve high-throughput scaling.