To make it easier for others to reproduce to build on our results, we strive to make our source code available under open access licenses. Here we will collect links to these released pieces of code.

Codebase for "Online Planning in POMDPs with Self-Improving Simulators": (to release soon)

Codebase for "Influence-Augmented Online Planning for Complex Environments":

Codebase for "Decentralized MCTS via Learned Teammate Models": environment algorithm 

Codebase for "Influence-Aware Memory Architectures for Deep Reinforcement Learning in POMDPs":

Codebase for "Loss Bounds for Approximate Influence-Based Abstraction":