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Imitation Learning via Off-Policy Distribution Matching
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Imitation Learning via Off-Policy Distribution Matching
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When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then using these ratios as rewards in a standard reinforcement learning (RL) algorithm. Traditionally, estimation of the distribution ratio requires on-policy data, which has caused previous work to either be exorbitantly data-inefficient or alter the original objective in a manner that can drastically change its optimum. In this work, we show how the original distribution ratio estimation objective may be transformed in a principled manner to yield a completely off-policy objective. In addition to the data-efficiency that this provides, we are able to show that this objective also renders the use of a separate RL optimization unnecessary.Rather, an imitation policy may be learned directly from this objective without the use of explicit rewards. We call the resulting algorithm ValueDICE and evaluate it on a suite of popular imitation learning benchmarks, finding that it can achieve state-of-the-art sample efficiency and performance.
Forward citations
Cited by 4 Pith papers
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Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
SeqRejectron builds a stopping rule from a small set of validator policies to achieve horizon-free sample-complexity guarantees for selective imitation learning under arbitrary train-test dynamics shifts.
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Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
SeqRejectron constructs a stopping rule with a small set of validator policies to achieve horizon-free sample complexity for selective imitation learning under arbitrary dynamics shifts.
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Distributional Inverse Reinforcement Learning
DistIRL recovers reward distributions and risk-aware policies from offline demonstrations by minimizing first-order stochastic dominance violations between agent and expert returns.
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Distributional Inverse Reinforcement Learning
A distributional offline IRL method minimizes first-order stochastic dominance violations to recover reward distributions and distribution-aware policies, with O(ε^{-2}) convergence and reported SOTA results on synthe...
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