457 research outputs found
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. However, it has been difficult to quantify progress in the
domain of continuous control due to the lack of a commonly adopted benchmark.
In this work, we present a benchmark suite of continuous control tasks,
including classic tasks like cart-pole swing-up, tasks with very high state and
action dimensionality such as 3D humanoid locomotion, tasks with partial
observations, and tasks with hierarchical structure. We report novel findings
based on the systematic evaluation of a range of implemented reinforcement
learning algorithms. Both the benchmark and reference implementations are
released at https://github.com/rllab/rllab in order to facilitate experimental
reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201
A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks
In recent years, large amounts of data have been generated, and computer
power has kept growing. This scenario has led to a resurgence in the interest
in artificial neural networks. One of the main challenges in training effective
neural network models is finding the right combination of hyperparameters to be
used. Indeed, the choice of an adequate approach to search the hyperparameter
space directly influences the accuracy of the resulting neural network model.
Common approaches for hyperparameter optimization are Grid Search, Random
Search, and Bayesian Optimization. There are also population-based methods such
as CMA-ES. In this paper, we present HBRKGA, a new population-based approach
for hyperparameter optimization. HBRKGA is a hybrid approach that combines the
Biased Random Key Genetic Algorithm with a Random Walk technique to search the
hyperparameter space efficiently. Several computational experiments on eight
different datasets were performed to assess the effectiveness of the proposed
approach. Results showed that HBRKGA could find hyperparameter configurations
that outperformed (in terms of predictive quality) the baseline methods in six
out of eight datasets while showing a reasonable execution time.Comment: 28 pages, 7 figure
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the achievable performance. To accelerate hyperparameter optimization,
we propose a generative model for the validation error as a function of
training set size, which is learned during the optimization process and allows
exploration of preliminary configurations on small subsets, by extrapolating to
the full dataset. We construct a Bayesian optimization procedure, dubbed
Fabolas, which models loss and training time as a function of dataset size and
automatically trades off high information gain about the global optimum against
computational cost. Experiments optimizing support vector machines and deep
neural networks show that Fabolas often finds high-quality solutions 10 to 100
times faster than other state-of-the-art Bayesian optimization methods or the
recently proposed bandit strategy Hyperband
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