Nicolas Heess

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

2015 · arXiv (Cornell University)

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. …

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

2018 · arXiv (Cornell University)

This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail …

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

2019 · arXiv (Cornell University)

Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our learning agents to quickly adapt to …

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

2020 · arXiv (Cornell University)

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with …

Evaluating model-based planning and planner amortization for continuous control

2021 · arXiv (Cornell University)

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We …