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.

Dead-simple self-learning offers developers a dead-simple approach to create and train reinforcement learning agents in Python. The framework abstracts core RL components, such as environment wrappers, policy modules, and experience buffers, into concise interfaces. Users can quickly initialize environments, define custom policies using familiar PyTorch or TensorFlow backends, and execute training loops with built-in logging and checkpointing. The library supports on-policy and off-policy algorithms, enabling flexible experimentation with Q-learning, policy gradients, and actor-critic methods. By reducing boilerplate code, dead-simple self-learning allows practitioners, educators, and researchers to prototype algorithms, test hypotheses, and visualize agent performance with minimal configuration. Its modular design also facilitates integration with existing ML stacks and custom environments.