Search Results for author:
Found 23 papers, 17 papers with code
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning
In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional training.
Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization
Toward the goal of ZSG to unseen variation in context, we propose the contextual recurrent state-space model (cRSSM), which introduces changes to the world model of the Dreamer (v3) (Hafner et al., 2023).
Hierarchical Transformers are Efficient Meta-Reinforcement Learners
We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach.
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines.
Automated Dynamic Algorithm Configuration
The performance of an algorithm often critically depends on its parameter configuration.
Contextualize Me -- The Case for Context in Reinforcement Learning
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
We extend this benchmark by analyzing optimal control policies that can select the parameters only from a given portfolio of possible values.
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents.
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance.
TempoRL: Learning When to Act
Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment.
Self-Paced Context Evaluation for Contextual Reinforcement Learning
Reinforcement learning (RL) has made a lot of advances for solving a single problem in a given environment; but learning policies that generalize to unseen variations of a problem remains challenging.
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance.
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts.
Hyperparameter Optimization
Model-based Reinforcement Learning
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In-Loop Meta-Learning with Gradient-Alignment Reward
To do this, we optimize the loss of the next training step.
MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments
We present MDP Playground, an efficient benchmark for Reinforcement Learning (RL) algorithms with various dimensions of hardness that can be controlled independently to challenge algorithms in different ways and to obtain varying degrees of hardness in generated environments.
Sample-Efficient Automated Deep Reinforcement Learning
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters.
Learning Heuristic Selection with Dynamic Algorithm Configuration
We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system.
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on parameter tuning.
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
We define a parameterised collection of fast-to-run toy environments in OpenAI Gym by varying these dimensions and propose to use these to understand agents better.
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning.
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours.
Towards White-box Benchmarks for Algorithm Control
The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on tuned hyperparameter configurations.
