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Found 15 papers, 7 papers with code
Hybrid Inverse Reinforcement Learning
In this work, we propose using hybrid RL -- training on a mixture of online and expert data -- to curtail unnecessary exploration.
The Virtues of Pessimism in Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations.
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Our approach is maximalist in that it provably handles non-Markovian, intransitive, and stochastic preferences while being robust to the compounding errors that plague offline approaches to sequential prediction.
Learning Shared Safety Constraints from Multi-task Demonstrations
Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect.
Inverse Reinforcement Learning without Reinforcement Learning
In this work, we demonstrate for the first time a more informed imitation learning reduction where we utilize the state distribution of the expert to alleviate the global exploration component of the RL subroutine, providing an exponential speedup in theory.
Game-Theoretic Algorithms for Conditional Moment Matching
A variety of problems in econometrics and machine learning, including instrumental variable regression and Bellman residual minimization, can be formulated as satisfying a set of conditional moment restrictions (CMR).
Sequence Model Imitation Learning with Unobserved Contexts
We consider imitation learning problems where the learner's ability to mimic the expert increases throughout the course of an episode as more information is revealed.
Minimax Optimal Online Imitation Learning via Replay Estimation
In the tabular setting or with linear function approximation, our meta theorem shows that the performance gap incurred by our approach achieves the optimal $\widetilde{O} \left( \min({H^{3/2}} / {N}, {H} / {\sqrt{N}} \right)$ dependency, under significantly weaker assumptions compared to prior work.
Causal Imitation Learning under Temporally Correlated Noise
We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions.
A Critique of Strictly Batch Imitation Learning
Recent work by Jarrett et al. attempts to frame the problem of offline imitation learning (IL) as one of learning a joint energy-based model, with the hope of out-performing standard behavioral cloning.
What Would the Expert $do(\cdot)$?: Causal Imitation Learning
Both approaches are able to find policies that match the result of a query to an unconfounded expert.
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching.
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets
We learn a model of the user's preferences from observations of the user's choices in easy settings with a few robots, and use it in challenging settings with more robots to automatically identify which robot the user would most likely choose to control, if they were able to evaluate the states of all robots at all times.
On the Utility of Model Learning in HRI
Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly?
Generative Models for Pose Transfer
We investigate nearest neighbor and generative models for transferring pose between persons.
