A Composable Specification Language for Reinforcement Learning Tasks

Understand

Reinforcement learning is a promising approach for learning control policies for robot tasks.

• However, specifying complex tasks (e.g., with multiple objectives and safety constraints) can be challenging, since the user must design a reward function that encodes the entire task.
• Furthermore, the user often needs to manually shape the reward to ensure convergence of the learning algorithm.
• We propose a language for specifying complex control tasks, along with an algorithm that compiles specifications in our language into a reward function and automatically performs reward shaping.