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Situationally Aware Options
Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Hierarchical abstractions, also known as options -- a type of temporally extended action (Sutton et. al. 1999) that enables a reinforcement learning agent to plan at a higher level…
Safer Classification by Synthesis
William Wang, Angelina Wang, Aviv Tamar +2
The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against…
Learning Generalized Reactive Policies using Deep Neural Networks
Edward Groshev, Maxwell Goldstein, Aviv Tamar +2
We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem insta…
A Machine Learning Approach to Routing
Asaf Valadarsky, Michael Schapira, Dafna Shahaf +1
Can ideas and techniques from machine learning be leveraged to automatically generate "good" routing configurations? We investigate the power of data-driven routing protocols. Our…
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar +3
We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challe…
Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar +1
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the rewar…
Shallow Updates for Deep Reinforcement Learning
Nir Levine, Tom Zahavy, Daniel J. Mankowitz +2
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This succes…