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Imitation Learning

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Papers

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Showing 1651–1675 of 2122 papers

TitleStatusHype
On Imitation in Mean-field Games—0
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions—0
Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs—0
Online Control-Informed Learning—0
Online Imitation Learning for Manipulation via Decaying Relative Correction through Teleoperation—0
Online Knowledge Distillation with Reward Guidance—0
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning—0
On-Policy Robot Imitation Learning from a Converging Supervisor—0
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents—0
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication—0
On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation—0
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator—0
On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration—0
On the Sample Complexity of a Policy Gradient Algorithm with Occupancy Approximation for General Utility Reinforcement Learning—0
On the Value of Interaction and Function Approximation in Imitation Learning—0
On Value Discrepancy of Imitation Learning—0
On Value Functions and the Agent-Environment Boundary—0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning—0
Open-TeleVision: Teleoperation with Immersive Active Visual Feedback—0
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks—0
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning—0
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone—0
Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning—0
Optimizing Crop Management with Reinforcement Learning and Imitation Learning—0
Orca 2: Teaching Small Language Models How to Reason—0
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