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Found 8 papers, 2 papers with code
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot
Replicating the remarkable athleticism seen in animals has long been a challenge in robotics control.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks.
Meta-Learning Parameterized Skills
We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks.
Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study
Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen the object before and having limited perception of the object's properties.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+3
Learning Generalizable Behavior via Visual Rewrite Rules
Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modifications of their environments or unfamiliar situations.
Bayesian Exploration for Lifelong Reinforcement Learning
A central question in reinforcement learning (RL) is how to leverage prior knowledge to accelerate learning in new tasks.
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates.
