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P.R. van der Vaart

4 records found

Efficient exploration is a major issue in reinforcement learning, particularly in environments with sparse rewards. In these environments, traditional methods like e-greedy fail to efficiently reach an optimal policy. A new method proposed by Fortunato, et al. Fortunato, et al. s ...
We present a large-scale empirical study of Bootstrapped DQN (BDQN) and Randomized-Prior BDQN (RP-BDQN) in the DeepSea environment, aimed at characterizing their scaling properties. Our primary contribution is a unified scaling law that accurately models the probability of reward ...
Deep Reinforcement Learning has achieved superhuman performance in many tasks, such as robotic control or autonomous driving. Algorithms in Deep Reinforcement Learning still suffer from a sample efficiency problem, where, in many cases, millions of samples are needed to achieve g ...
This paper investigates how Random Network Distillation (RND), coupled with Boltzmann exploration, influences exploration behaviour and learning dynamics in value-based agents such as Deep Q-Learning (DQN) across a range of environments, from classic control tasks to behaviour su ...