rllab provides a structured environment for building and testing reinforcement learning algorithms. It includes implementations of classic RL algorithms such as REINFORCE and Truncated Natural Policy Gradient, supports TensorFlow, and integrates with TensorBoard for monitoring training progress. The framework is fully compatible with the OpenAI Gym environment interface, allowing researchers to benchmark algorithms on standardized continuous control tasks.