Learning more skills through optimistic exploration

no code implementations • ICLR 2022 • DJ Strouse, Kate Baumli, David Warde-Farley, Vlad Mnih, Steven Hansen

However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective.

Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning

no code implementations • ICLR 2019 • catalin ionescu, tejas kulkarni, aaron van de oord, andriy mnih, Vlad Mnih

Exploration in environments with sparse rewards is a key challenge for reinforcement learning.

Atari Games Hierarchical Reinforcement Learning +3

Noisy Networks for Exploration

15 code implementations • ICLR 2018 • Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration.

Atari Games Efficient Exploration +2