Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition

Understand

Many environments contain numerous available niches of variable value, each associated with a different local optimum in the space of behaviors (policy space).

• In such situations it is often difficult to design a learning process capable of evading distraction by poor local optima long enough to stumble upon the best available niche.
• In this work we propose a generic reinforcement learning (RL) algorithm that performs better than baseline deep Q-learning algorithms in such environments with multiple variably-valued niches.
• The algorithm we propose consists of two parts: an agent architecture and a learning rule.