# Introspection Learning

## TL;DR

Introspection Learning is a novel reinforcement learning approach that enables policies to self-assess potential situations and actions without environment interaction, enhancing training efficiency and robustness.

## Contribution

The paper introduces Introspection Learning, a policy-questioning algorithm that is agnostic to RL methods and can improve training speed and safety robustness.

## Key findings

- Accelerates training process
- Enhances policy robustness to safety constraints
- Provides insights into policy health

## Abstract

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.