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Found 11 papers, 5 papers with code
ETA Prediction with Graph Neural Networks in Google Maps
Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.
An empirical investigation of the challenges of real-world reinforcement learning
We believe that an approach that addresses our set of proposed challenges would be readily deployable in a large number of real world problems.
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.
Towards Consistent Performance on Atari using Expert Demonstrations
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Challenges of Real-World Reinforcement Learning
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios.
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning
Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing.
Robotics
Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Safe Exploration in Continuous Action Spaces
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated.
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards.
Deep Q-learning from Demonstrations
We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.
Adaptive Lambda Least-Squares Temporal Difference Learning
Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in the field of reinforcement learning.
