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Found 5 papers, 5 papers with code
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
In this work, we introduce a novel learning algorithm called SafeAPT that leverages a diverse repertoire of policies evolved in the simulation and transfers the most promising safe policy to the real robot through episodic interaction.
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points.
Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics
Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e. g., a damaged robot, a new object, etc.).
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties.
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.
