9 citations · 15 across the 3 of their papers we have counts for
5 papers
Learning in Sparse Rewards settings through Quality-Diversity algorithms
Giuseppe Paolo
In the Reinforcement Learning (RL) framework, the learning is guided through a reward signal. This means that in situations of sparse rewards the agent has to focus on exploration,…
Sparse Reward Exploration via Novelty Search and Emitters
Giuseppe Paolo, Alexandre Coninx, Stephane Doncieux +1
Reward-based optimization algorithms require both exploration, to find rewards, and exploitation, to maximize performance. The need for efficient exploration is even more significa…
Novelty Search makes Evolvability Inevitable
Stephane Doncieux, Giuseppe Paolo, Alban Laflaquière +1
Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to s…
Unsupervised Learning and Exploration of Reachable Outcome Space
Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx +1
Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning pro…
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
Giuseppe Paolo, Lei Tai, Ming Liu
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep…