A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks

Understand

Reward engineering is an important aspect of reinforcement learning.

• Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome.
• Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior.
• This operation can be expensive when exploration requires systems to interact with the physical world.