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Reinforcement Learning

Choreographer: Learning and Adapting Skills in Imagination
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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Implicit Offline Reinforcement Learning via Supervised Learning
Implicit Offline Reinforcement Learning via Supervised Learning
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset of …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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Using Confounded Data in Offline RL
Using Confounded Data in Offline RL
In this work we consider the problem of confounding in offline RL, also referred to as the delusion problem. While it is known that …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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Scaling up ML-based Black-box Planning with Partial STRIPS Models
Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like …
Workshop at International Join Conference on Artificial Intelligence (IJCAI),  2022.
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Direct Behavior Specification via Constrained Reinforcement Learning
Direct Behavior Specification via Constrained Reinforcement Learning
The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors. …
International Conference on Machine Learning (ICML),  2022.
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Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels
Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels
Reinforcement learning (RL) aims at autonomously performing complex tasks. To this end, a reward signal is used to steer the learning …
Workshop at the International Conference on Machine Learning (ICML),  2022.
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Scaling up ML-based Black-box Planning with Partial STRIPS Models
Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like …
ICAPS'22 Workshop on Reliable Data-Driven Planning and Scheduling,  2022.
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A Probabilistic Perspective on Reinforcement Learning via Supervised Learning
A Probabilistic Perspective on Reinforcement Learning via Supervised Learning
Reinforcement Learning via Supervised Learning (RvS) only uses supervised techniques to learn desirable behaviors from large datasets. …
Workshop at the International Conference on Learning Representations (ICLR),  2022.
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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the …
Conference on Neural Information Processing Systems (NeurIPS),  2021.
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Reinforcement Learning with Random Delays
Reinforcement Learning with Random Delays
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study …
International Conference on Learning Representations (ICLR),  2021.
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