57 citations · 98 across the 7 of their papers we have counts for
6 papers · 1 filter
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
Melrose Roderick, Felix Berkenkamp, Fatemeh Sheikholeslami +1
In many real-world problems, there is a limited set of training data, but an abundance of unlabeled data. We propose a new method, Generative Posterior Networks (GPNs), that uses u…
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning
Melrose Roderick, Gaurav Manek, Felix Berkenkamp +1
A key problem in off-policy Reinforcement Learning (RL) is the mismatch, or distribution shift, between the dataset and the distribution over states and actions visited by the lear…
Enforcing robust control guarantees within neural network policies
Priya L. Donti, Melrose Roderick, Mahyar Fazlyab +1
When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rig…
Provably Safe PAC-MDP Exploration Using Analogies
Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter
A key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration (needed to attain good performance on the task) with safet…
Implementing the Deep Q-Network
Melrose Roderick, James MacGlashan, Stefanie Tellex
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research. However, replicating results for complex…
Deep Abstract Q-Networks
Melrose Roderick, Christopher Grimm, Stefanie Tellex
We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 d…