Emergent Communication in Agents is an open-source PyTorch framework designed for researchers exploring how multi-agent systems develop their own communication protocols. The library offers flexible implementations of cooperative reinforcement learning tasks, including referential games, combination games, and object identification challenges. Users define speaker and listener agent architectures, specify message channel properties like vocabulary size and sequence length, and select training strategies such as policy gradients or supervised learning. The framework includes end-to-end scripts for running experiments, analyzing communication efficiency, and visualizing emergent languages. Its modular design allows easy extension with new game environments or custom loss functions. Researchers can reproduce published studies, benchmark new algorithms, and probe compositionality and semantics of emergent agent languages.

RL Shooter is a Python-based framework that integrates ViZDoom with OpenAI Gym APIs to create a flexible reinforcement learning environment for FPS games. Users can define custom scenarios, maps, and reward structures to train agents on navigation, target detection, and shooting tasks. With configurable observation frames, action spaces, and logging facilities, it supports popular deep RL libraries such as Stable Baselines and RLlib, enabling clear performance tracking and reproducibility across experiments.