6 citations · 8 across the 7 of their papers we have counts for
3 papers · 1 filter
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Jakob Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the sc…
How do Offline Measures for Exploration in Reinforcement Learning behave?
Jakob J. Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Sufficient exploration is paramount for the success of a reinforcement learning agent. Yet, exploration is rarely assessed in an algorithm-independent way. We compare the behavior…
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
Jakob J. Hollenstein, Erwan Renaudo, Matteo Saveriano +1
Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum. Furthermore, the availability o…