Backpropagating Through Simulation: Analytic Policy Gradients for Sample and Learning Efficient Differentiable Continuous Control

The article introduces Analytic Policy Gradients (APG), a method that allows for exact gradient computation in model-free reinforcement learning by backpropagating through differentiable environment dynamics, contrasting with Proximal Policy Optimization (PPO) which relies on high-variance sampled rewards. APG was evaluated on four continuous control tasks, demonstrating improved sample efficiency by employing a multi-axis evaluation protocol that separates performance metrics based on environment and gradient steps. This approach is significant for practitioners as it enhances learning efficiency in complex environments, potentially reducing the number of interactions needed for effective policy training.