99 citations · 114 across the 7 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021
Discovering and Exploiting Sparse Rewards in a Learned Behavior Space
Giuseppe Paolo, Miranda Coninx, Alban Laflaquière +1
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strateg…
cs.NE2021★ 9 cited
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…