Optimistic Simulated Exploration as an Incentive for Real Exploration
Abstract
Description
Many reinforcement learning exploration techniques are overly optimistic and try to explore every state. Such exploration is impossible in environments with the unlimited number of states. I propose to use simulated exploration with an optimistic model to discover promising paths for real exploration. This reduces the needs for the real exploration.
accepted, noted that the initial path was 217 steps long
accepted, noted that the initial path was 217 steps long