DisTop: Discovering a Topological representation to learn diverse and rewarding skills

Understand

The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states.

• Following this,we introduce DisTop, a new model that simultaneously learns diverse skills and focuses on improving rewarding skills.
• DisTop progressively builds a discrete topology of the environment using an unsupervised contrastive loss, a growing network and a goal-conditioned policy.
• Using this topology, a state-independent hierarchical policy can select where the agent has to keep discovering skills in the state space.