Conditional Kernel Imitation Learning For Continuous State Environments

Abstract

Imitation Learning (IL) is an important paradigm within the broader reinforcement learning (RL) methodology. Unlike most of RL, it does not assume availability of reward-feedback. Reward inference and shaping are known to be difficult and error-prone methods particularly when the demonstration data comes from human experts. Classical methods such as behavioral cloning and inverse reinforcement learning are highly sensitive to estimation errors, a problem that is particularly acute in continuous state space problems. Meanwhile, state-of-the-art IL algorithms convert behavioral policy learning problems into distribution-matching problems which often require additional online interaction data to be effective. In this paper, we consider the problem of imitation learning in continuous state space environments based solely on observed behavior, without access to transition dynamics information, reward structure, or, most importantly, any additional interactions with the environment. Our appr

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyagrawal2023conditional

Related papers

• State-only Imitation With Transition Dynamics Mismatch (2020)0.00
• Causal Imitation Learning Under Measurement Error And Distribution Shift (2026)0.00
• Fully General Online Imitation Learning (2021)0.00
• Inverse Reinforcement Learning In A Continuous State Space With Formal Guarantees (2021)0.00
• The Pitfalls Of Imitation Learning When Actions Are Continuous (2025)0.00
• Invariant Causal Imitation Learning For Generalizable Policies (2023)0.00
• A Bayesian Solution To The Imitation Gap (2024)0.00
• Iq-learn: Inverse Soft-q Learning For Imitation (2021)0.00