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03/26/2021
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
In the past decade, model-free reinforcement learning (RL) has provided ...
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10/09/2019
Compatible features for Monotonic Policy Improvement
Recent policy optimization approaches have achieved substantial empirica...
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10/09/2019
Policy Optimization Through Approximated Importance Sampling
Recent policy optimization approaches (Schulman et al., 2015a, 2017) hav...
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06/21/2019
Disentangled Skill Embeddings for Reinforcement Learning
We propose a novel framework for multi-task reinforcement learning (MTRL...
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09/06/2018
Model-Based Stabilisation of Deep Reinforcement Learning
Though successful in high-dimensional domains, deep reinforcement learni...
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02/19/2018
Learning High-level Representations from Demonstrations
Hierarchical learning (HL) is key to solving complex sequential decision...
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11/10/2017
Learning with Options that Terminate Off-Policy
A temporally abstract action, or an option, is specified by a policy and...
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08/22/2017
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Many real-world reinforcement learning problems have a hierarchical natu...
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08/01/2017
Forecasting day-ahead electricity prices in Europe: the importance of considering market integration
Motivated by the increasing integration among electricity markets, in th...
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02/28/2017
Analysing Congestion Problems in Multi-agent Reinforcement Learning
Congestion problems are omnipresent in today's complex networks and repr...
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12/17/2015
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments
This paper explores the performance of fitted neural Q iteration for rei...
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02/11/2015
Off-Policy Reward Shaping with Ensembles
Potential-based reward shaping (PBRS) is an effective and popular techni...
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05/21/2014