palaestrAI is an universal framework for multi-agent artificial intelligence, including, but not limited to, deep reinforcement learning. It allows for cooperative, adversarial, and just plain any interaction between agents in diverse environments. palaestrAI is a loosely coupled, distributed system. It allows to use any kind of algorithm in concert: Think A3C agents playing against Q-Learning counterparts who cooperate with ACKTR-based agents.

palaestrAIs mosaik bridge makes integration with any environment and existing co-simulation setups easy. palaestrAI provides an infrastructure for reliable and reproducibly experimentation through experiment studies, efficient experiment space sampling methods, and serialization of all experiment instances with a fitting store backend.

palaestrAI has many use cases: Training of agents on any given environment, e.g., for the analysis of complex Cyber-Physical Systems, resilient operation of critical infrastructures, market analysis, ICT system attack analysis and hardening, market design, and even building blocks of Artificial General Intelligence

The palaestrAI ecosystem forms the reference implementation of the Adversarial Resilience Learning methodology.

Modules of the palaestrAI Framework