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cs.LG2022
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,…
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…