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arxiv: 2511.07288 · v2 · pith:BFBVFIO2new · submitted 2025-11-10 · 💻 cs.LG · cs.AI

Enabling Off-Policy Imitation Learning with Deep Actor Critic Stabilization

Sayambhu Sen , Shalabh Bhatnagar This is my paper

Pith reviewed 2026-05-21 18:30 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords imitation learningoff-policy learningadversarial imitationreinforcement learningsample efficiencydouble Q-networkactor-critic
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The pith

Off-policy updates with double Q stabilization let adversarial imitation learning match experts using fewer samples.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard imitation learning from demonstrations avoids reward engineering but remains sample-inefficient because leading methods rely on on-policy algorithms such as TRPO. This paper replaces the on-policy core with an off-policy actor-critic structure while adding double Q-network stabilization and value estimation that does not require explicit reward recovery. The resulting algorithm is shown to reach expert-level performance with substantially fewer environment interactions. A sympathetic reader would care because real-world demonstration data and robot rollouts are expensive; any method that lowers the interaction budget makes imitation learning more practical for complex tasks.

Core claim

The paper introduces an adversarial imitation learning algorithm that incorporates off-policy learning. By combining an off-policy framework with double Q-network based stabilization and value learning without reward function inference, the method reduces the samples required to robustly match expert behavior.

What carries the argument

Off-policy actor-critic structure augmented with double Q-network stabilization and reward-free value learning inside an adversarial imitation objective.

Load-bearing premise

Double Q-network stabilization and off-policy updates can be combined with adversarial imitation learning without creating new instability or bias that blocks expert matching.

What would settle it

Benchmarks on standard continuous-control tasks where the algorithm either requires as many or more samples than GAIL to reach expert performance or exhibits divergence during training.

Figures

Figures reproduced from arXiv: 2511.07288 by Sayambhu Sen, Shalabh Bhatnagar.

Figure 1
Figure 1. Figure 1: Using noise sampling after output vs Using noise as input and giving a bounded action [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Off-policy actor critic architecture for continuous control [12] [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: BipedalWalker-v2 environment in OpenAI gym [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training performance of our off-policy imitation algorithm (blue) compared to the GAIL [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
read the original abstract

Learning complex policies with Reinforcement Learning (RL) is often hindered by instability and slow convergence, a problem exacerbated by the difficulty of reward engineering. Imitation Learning (IL) from expert demonstrations bypasses this reliance on rewards. However, state-of-the-art IL methods, exemplified by Generative Adversarial Imitation Learning (GAIL)Ho et. al, suffer from severe sample inefficiency. This is a direct consequence of their foundational on-policy algorithms, such as TRPO Schulman et.al. In this work, we introduce an adversarial imitation learning algorithm that incorporates off-policy learning to improve sample efficiency. By combining an off-policy framework with auxiliary techniques specifically, in this case a double Q network based stabilization and value learning without reward function inference we demonstrate a reduction in the samples required to robustly match expert behavior.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces an off-policy adversarial imitation learning algorithm that augments a GAIL-style framework with an off-policy actor-critic backbone, double Q-network stabilization, and value learning that avoids explicit reward inference, claiming this combination reduces the number of samples needed to robustly match expert behavior.

Significance. If the central claim holds, the work would offer a practical route to sample-efficient imitation learning in settings where on-policy rollouts are expensive. The explicit use of double-Q stabilization and off-policy replay to address known instabilities in deep actor-critic methods is a targeted contribution that could extend to other adversarial IL variants.

major comments (2)
  1. Abstract: the assertion that the method 'demonstrate[s] a reduction in the samples required to robustly match expert behavior' is presented without any experimental details, results, baselines, or error bars; the central claim therefore rests on an unverified assertion rather than shown evidence.
  2. Proposed algorithm (off-policy discriminator updates): the claim that double-Q stabilization plus off-policy replay can be combined with the adversarial objective without introducing new bias is load-bearing for the sample-efficiency result, yet the manuscript does not isolate whether the discriminator is trained on the current policy's occupancy measure or on stale replay-buffer data; double-Q corrects Q-overestimation but leaves the joint state-action distribution mismatch unaddressed, which is the precise source of bias that on-policy GAIL avoids.
minor comments (1)
  1. The abstract would be clearer if it named the specific environments or tasks on which the sample-efficiency improvement was observed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for their insightful comments, which have helped us improve the clarity and rigor of our manuscript. Below, we provide detailed responses to each major comment and outline the revisions we plan to make.

read point-by-point responses

  1. Referee: Abstract: the assertion that the method 'demonstrate[s] a reduction in the samples required to robustly match expert behavior' is presented without any experimental details, results, baselines, or error bars; the central claim therefore rests on an unverified assertion rather than shown evidence.

    Authors: We agree with this observation. The abstract in the current version is concise but lacks supporting details for the claim. In the revised manuscript, we will expand the abstract to briefly describe the experimental validation, including the use of standard benchmarks, comparison against GAIL and other baselines, and reference to quantitative results showing sample efficiency improvements with error bars from multiple runs. revision: yes

  2. Referee: Proposed algorithm (off-policy discriminator updates): the claim that double-Q stabilization plus off-policy replay can be combined with the adversarial objective without introducing new bias is load-bearing for the sample-efficiency result, yet the manuscript does not isolate whether the discriminator is trained on the current policy's occupancy measure or on stale replay-buffer data; double-Q corrects Q-overestimation but leaves the joint state-action distribution mismatch unaddressed, which is the precise source of bias that on-policy GAIL avoids.

    Authors: This is a valid concern regarding the potential for bias in the adversarial training due to off-policy data. In our method, the discriminator is updated using state-action pairs sampled from the replay buffer, which reflects the behavior of the current policy as the buffer is populated with recent rollouts. To address the distribution mismatch, we incorporate periodic policy updates and rely on the stabilization provided by double Q-networks for the value estimation in the actor-critic component. We will revise the manuscript to include a more detailed description of the discriminator training procedure, an explicit discussion of how bias is mitigated, and potentially an ablation study isolating the effect of off-policy updates on the discriminator. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical combination claim with no visible equations or self-referential reductions

full rationale

The abstract and description present the contribution as an empirical algorithm that combines an off-policy actor-critic framework with double-Q stabilization and value learning (without reward inference) to reduce samples needed for matching expert behavior in adversarial IL. No equations, derivations, or load-bearing mathematical steps are visible in the provided text. No self-citations, fitted parameters renamed as predictions, or ansatzes smuggled via prior work are described. The central claim is a demonstration of sample-efficiency improvement rather than a derivation that reduces to its own inputs by construction. Per hard rules, circularity requires explicit quotes exhibiting reduction (e.g., a prediction equivalent to a fit); absent that, the finding is no significant circularity. This is the most common honest outcome for papers whose core contribution is algorithmic combination and empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, no explicit free parameters, ad-hoc axioms, or invented entities are described; the work appears to rely on standard RL assumptions such as Markov decision processes and the existence of expert demonstrations.

axioms (1)
  • standard math Standard MDP and policy optimization assumptions underlying off-policy RL and adversarial IL
    Implicit in any RL/IL method; invoked by reference to GAIL and TRPO frameworks.

pith-pipeline@v0.9.0 · 5662 in / 1171 out tokens · 34494 ms · 2026-05-21T18:30:14.258135+00:00 · methodology

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Reference graph

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