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Found 9 papers, 5 papers with code
Towards Continual Reinforcement Learning for Quadruped Robots
Quadruped robots have emerged as an evolving technology that currently leverages simulators to develop a robust controller capable of functioning in the real-world without the need for further training.
Continual Learning on the Edge with TensorFlow Lite
In addition, we expand the TensorFlow Lite library to include continual learning capabilities, by integrating a simple replay approach into the head of the current transfer learning model.
Quality-Diversity Optimization: a novel branch of stochastic optimization
In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community.
A survey on policy search algorithms for learning robot controllers in a handful of trials
Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot.
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Evolution has produced an astonishing diversity of species, each filling a different niche.
Black-Box Data-efficient Policy Search for Robotics
The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties.
Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors
We compare our new "safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in which the safety constraints are dealt as separate objectives.
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run.
Reset-free Trial-and-Error Learning for Robot Damage Recovery
However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously.
