Reinforcement Learning
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp
Neural Information Processing Systems http://nips.cc/
Learning Safe Policies with Expert Guidance
Jessie Huang, Fa Wu, Doina Precup, Yang Cai
Neural Information Processing Systems http://nips.cc/
DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning
Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, Bo Zhang
Neural Information Processing Systems http://nips.cc/
Discovering Creative Behaviors through DUPLEX: Diverse Universal Features for Policy Exploration
The ability to approach the same problem from different angles is a cornerstone of human intelligence that leads to robust solutions and effective adaptation to problem variations. In contrast, current RL methodologies tend to lead to policies that settle on a single solution to a given problem, making them brittle to problem variations. Replicating human flexibility in reinforcement learning agents is the challenge that we explore in this work.
Geometric Algebra Transformer
Such data can take numerous forms, for instance points, direction vectors, translations, or rotations, but to date there is no single architecture that can be applied to such a wide variety of geometric types while respecting their symmetries. In this paper we introduce the Geometric Algebra Transformer (GA Tr), a general-purpose architecture for geometric data.
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
The key ingredient to MixTURE's success is automatically learning
StabilizingOff-PolicyQ-LearningviaBootstrapping ErrorReduction
One of the primary drivers of the success of machine learning methods in open-world perception settings, such ascomputer vision [19]and NLP [8],has been the ability ofhigh-capacity function approximators, suchasdeepneuralnetworks,tolearngeneralizable modelsfromlargeamountsof data.
Reinforcement Learning for Solving the Vehicle Routing Problem
MohammadReza Nazari, Afshin Oroojlooy, Lawrence Snyder, Martin Takac
Neural Information Processing Systems http://nips.cc/
A Proofs 438 We first redefine notation for clarity and then provide the proofs of the results in the main paper
We first redefine notation for clarity and then provide the proofs of the results in the main paper. Now we first prove that the iteration in Eq.2 has a fixed point. Proof of Lemma 3.1: Let We present the bound on using empirical Bellman operator compared to the true Bellman operator. The proof can be found in [6]. Proof of Theorem 3.4: Recall that the expression of the V -function iterate is given by: Proof of Theorem 3.6: The proof of this statement is divided into two parts.
