DemPref uses demonstrations to form a coarse reward prior and ground active preference queries, achieving higher efficiency than pure preference learning and higher user preference than IRL in experiments.
Risk-Aware Active Inverse Reinforcement Learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. Utilizing recent advances in performance bounds for inverse reinforcement learning, we propose a risk-aware active inverse reinforcement learning algorithm that focuses active queries on areas of the state space with the potential for large generalization error. We show that risk-aware active learning outperforms standard active IRL approaches on gridworld, simulated driving, and table setting tasks, while also providing a performance-based stopping criterion that allows a robot to know when it has received enough demonstrations to safely perform a task.
fields
cs.RO 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
DynoPlan adds dynamics models and a demonstration-derived heuristic to the options framework so that hierarchical RL can switch between motion planning and DNN controllers via short-horizon model-predictive evaluation.
citing papers explorer
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Learning Reward Functions by Integrating Human Demonstrations and Preferences
DemPref uses demonstrations to form a coarse reward prior and ground active preference queries, achieving higher efficiency than pure preference learning and higher user preference than IRL in experiments.
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DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL
DynoPlan adds dynamics models and a demonstration-derived heuristic to the options framework so that hierarchical RL can switch between motion planning and DNN controllers via short-horizon model-predictive evaluation.