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Found 4 papers, 3 papers with code
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
We propose a new method for count-based exploration in high-dimensional state spaces.
Scaling Goal-based Exploration via Pruning Proto-goals
One of the gnarliest challenges in reinforcement learning (RL) is exploration that scales to vast domains, where novelty-, or coverage-seeking behaviour falls short.
Skill Discovery for Exploration and Planning using Deep Skill Graphs
Temporal abstraction provides an opportunity to drastically lower the decision making burden facing reinforcement learning agents in rich sensorimotor spaces.
Option Discovery using Deep Skill Chaining
Autonomously discovering temporally extended actions, or skills, is a longstanding goal of hierarchical reinforcement learning.
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