Introspection Learning
This approach addresses the need for more efficient and safe reinforcement learning, though it appears incremental as it builds on existing methods without introducing a new paradigm.
The paper tackles the problem of synthesizing experience for reinforcement learning agents without environmental interaction by asking policies about hypothetical situations and actions, resulting in a method that speeds up training and improves robustness to safety constraints.
Traditional reinforcement learning agents learn from experience, past or present, gained through interaction with their environment. Our approach synthesizes experience, without requiring an agent to interact with their environment, by asking the policy directly "Are there situations X, Y, and Z, such that in these situations you would select actions A, B, and C?" In this paper we present Introspection Learning, an algorithm that allows for the asking of these types of questions of neural network policies. Introspection Learning is reinforcement learning algorithm agnostic and the states returned may be used as an indicator of the health of the policy or to shape the policy in a myriad of ways. We demonstrate the usefulness of this algorithm both in the context of speeding up training and improving robustness with respect to safety constraints.