RL-Agents is a research-grade reinforcement learning framework built on PyTorch that bundles popular RL algorithms across value-based, policy-based, and actor-critic methods. The library features a modular agent API, GPU acceleration, seamless integration with OpenAI Gym, and built-in logging and visualization tools. Users can configure hyperparameters, customize training loops, and benchmark performance with a few lines of code, making RL-Agents ideal for academic research, prototyping, and industrial experimentation.

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.