Acme is a Python-based framework that simplifies the development and evaluation of reinforcement learning agents. It offers a collection of prebuilt agent implementations (e.g., DQN, PPO, SAC), environment wrappers, replay buffers, and distributed execution engines. Researchers can mix and match components to prototype new algorithms, monitor training metrics with built-in logging, and leverage scalable distributed pipelines for large-scale experiments. Acme integrates with TensorFlow and JAX, supports custom environments via OpenAI Gym interfaces, and includes utilities for checkpointing, evaluation, and hyperparameter configuration.

Multi-Agent Reinforcement Learning by alaamoheb is a comprehensive open-source library designed to facilitate the development, training, and evaluation of multiple agents acting in shared environments. It includes modular implementations of value-based and policy-based algorithms such as DQN, PPO, MADDPG, and more. The repository supports integration with OpenAI Gym, Unity ML-Agents, and the StarCraft Multi-Agent Challenge, allowing users to experiment in both research and real-world inspired scenarios. With configurable YAML-based experiment setups, logging utilities, and visualization tools, practitioners can monitor learning curves, tune hyperparameters, and compare different algorithms. This framework accelerates experimentation in cooperative, competitive, and mixed multi-agent tasks, streamlining reproducible research and benchmarking.