ServiceNow AI Research

Reinforcement Learning

Searching for Markovian Subproblems to Address Partially Observable Reinforcement Learning
Searching for Markovian Subproblems to Address Partially Observable Reinforcement Learning
In partially observable environments, an agent’s policy should often be a function of the history of its interaction with the …
Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM),  2019.
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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and …
International Conference on Learning Representations (ICLR),  2019.
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Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In …
Association for the Advancement of Artificial Intelligence (AAAI),  2019.
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Reinforced Imitation in Heterogeneous Action Space
Reinforced Imitation in Heterogeneous Action Space
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we …
Workshop at the Neural Information Processing Systems (NeurIPS),  2018.
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LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning
LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning
In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many …
International Join Conference on Artificial Intelligence (IJCAI),  2018.
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Teaching Multiple Tasks to an RL Agent using LTL
Teaching Multiple Tasks to an RL Agent using LTL
This paper examines the problem of how to teach multiple tasks to a Reinforcement Learning (RL) agent. To this end, we use Linear …
Autonomous Agents and Multiagent Systems (AAMAS),  2018.
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Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
In this paper we propose Reward Machines – a type of finite state machine that supports the spec- ification of reward functions while …
International Conference on Machine Learning (ICML),  2018.
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Advice-Based Exploration in Model-Based Reinforcement Learning
Advice-Based Exploration in Model-Based Reinforcement Learning
Convergence to an optimal policy using model-based rein- forcement learning can require significant exploration of the environment. In …
Canadian Conference on AI,  2018.
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