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12/13/2022
Improving generalization in reinforcement learning through forked agents
An eco-system of agents each having their own policy with some, but limi...
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12/05/2022
A Machine with Short-Term, Episodic, and Semantic Memory Systems
Inspired by the cognitive science theory of the explicit human memory sy...
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10/31/2022
Disentangled (Un)Controllable Features
In the context of MDPs with high-dimensional states, reinforcement learn...
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07/18/2022
A Meta-Reinforcement Learning Algorithm for Causal Discovery
Causal discovery is a major task with the utmost importance for machine ...
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04/13/2022
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents
Adapting a Reinforcement Learning (RL) agent to an unseen environment is...
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04/04/2022
A Machine With Human-Like Memory Systems
Inspired by the cognitive science theory, we explicitly model an agent w...
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11/22/2021
Component Transfer Learning for Deep RL Based on Abstract Representations
In this work we investigate a specific transfer learning approach for de...
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09/28/2021
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey
Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have su...
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02/14/2021
Domain Adversarial Reinforcement Learning
We consider the problem of generalization in reinforcement learning wher...
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09/28/2020
Novelty Search in representational space for sample efficient exploration
We present a new approach for efficient exploration which leverages a lo...
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03/02/2020
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Almost all neural architecture search methods are evaluated in terms of ...
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09/14/2019
Neural Architecture Search for Class-incremental Learning
In class-incremental learning, a model learns continuously from a sequen...
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11/30/2018
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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09/12/2018
Combined Reinforcement Learning via Abstract Representations
In the quest for efficient and robust reinforcement learning methods, bo...
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05/09/2018
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
In reinforcement learning (RL), stochastic environments can make learnin...
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09/22/2017
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
This paper stands in the context of reinforcement learning with partial ...
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12/07/2015
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
Using deep neural nets as function approximator for reinforcement learni...
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06/30/2014