Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
This work addresses the challenge of high measurement costs in scientific domains like materials design, potentially lowering barriers to applying RL in real-world settings, though it is incremental as it adapts existing RL methods.
The paper tackles the problem of costly state measurements in reinforcement learning for scientific applications by proposing a framework that incorporates measurement costs into the reward, enabling agents to learn policies that reduce measurements by up to 50% while maintaining optimal action policies.
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing. A major challenge, however, lies in fact that measuring the state of the system is often costly and time consuming in scientific applications, whereas policy learning with RL requires a measurement after each time step. In this work, we make the measurement costs explicit in the form of a costed reward and propose a framework that enables off-the-shelf deep RL algorithms to learn a policy for both selecting actions and determining whether or not to measure the current state of the system at each time step. In this way, the agents learn to balance the need for information with the cost of information. Our results show that when trained under this regime, the Dueling DQN and PPO agents can learn optimal action policies whilst making up to 50\% fewer state measurements, and recurrent neural networks can produce a greater than 50\% reduction in measurements. We postulate the these reduction can help to lower the barrier to applying RL to real-world scientific applications.