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arxiv: 1906.08387 · v1 · pith:HV6QLK3Bnew · submitted 2019-06-19 · 💻 cs.LG · stat.ML

Experience Replay Optimization

Daochen Zha , Kwei-Herng Lai , Kaixiong Zhou , Xia Hu This is my paper

Pith reviewed 2026-05-25 20:01 UTC · model grok-4.3

classification 💻 cs.LG stat.ML
keywords experience replayreinforcement learningoff-policy algorithmsreplay policycontinuous control
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The pith

Learning a replay policy by alternating updates with the agent policy improves off-policy reinforcement learning performance.

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

The paper establishes that experience replay in off-policy RL can be improved by training a separate replay policy whose goal is to select past experiences that maximize the agent's eventual cumulative reward. Current uniform or rule-based replay strategies are treated as potentially suboptimal, so the method instead learns the selection process directly. Challenges of noisy large buffers and unstable reward signals are addressed by alternately updating the agent policy on replayed data and then updating the replay policy to supply better data. Experiments on continuous control tasks are presented as evidence that this yields measurable gains over standard replay methods.

Core claim

The central claim is that a replay policy can be learned to optimize the cumulative reward by alternately updating two policies: the agent policy is trained to maximize reward from the selected experiences, while the replay policy is trained to choose the most useful experiences from the memory buffer for the agent.

What carries the argument

The ERO framework that alternately updates the agent policy on replayed data and the replay policy to supply experiences maximizing the agent's cumulative reward.

If this is right

  • Off-policy algorithms gain a learned mechanism for prioritizing experiences instead of relying on fixed rules.
  • The alternating update scheme enables the replay policy to adapt as the agent improves.
  • Performance gains appear across multiple continuous control environments when the replay policy is optimized this way.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same alternating-optimization idea could be tested in settings with very large replay buffers where manual prioritization becomes impractical.
  • If the replay policy generalizes across tasks, it might reduce the need for environment-specific replay heuristics.
  • Extending the approach to discrete-action or model-based RL would test whether the core alternation pattern transfers.

Load-bearing premise

A replay policy can be stably learned and provide net benefit despite the replay memory being noisy and large and the cumulative reward signal being unstable.

What would settle it

Running the method on standard continuous control benchmarks and finding that final performance or sample efficiency does not exceed that of uniform replay or prioritized experience replay.

Figures

Figures reproduced from arXiv: 1906.08387 by Daochen Zha, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu.

Figure 1
Figure 1. Figure 1: An overview of experience replay optimization (ERO). [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Each task is run for 5 times with 2 × 106 timesteps using different random seeds, and the average return over episodes is reported. We make the following observations. First, the proposed ERO consistently outperforms all the baselines on most of the continuous control tasks in terms of sample efficiency. On HalfCheetah, InvertedPendulum, and InvertedDoublePendulum, ERO performs clearly better than Vanilla-… view at source ↗
Figure 2
Figure 2. Figure 2: Performance comparison of ERO against baselines on 8 continuous control tasks. The shaded area represents mean [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of running time in seconds for ERO and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The evolution of TD error (top), timestep difference (mid [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand. Contemporary off-policy algorithms either replay past experiences uniformly or utilize a rule-based replay strategy, which may be sub-optimal. In this work, we consider learning a replay policy to optimize the cumulative reward. Replay learning is challenging because the replay memory is noisy and large, and the cumulative reward is unstable. To address these issues, we propose a novel experience replay optimization (ERO) framework which alternately updates two policies: the agent policy, and the replay policy. The agent is updated to maximize the cumulative reward based on the replayed data, while the replay policy is updated to provide the agent with the most useful experiences. The conducted experiments on various continuous control tasks demonstrate the effectiveness of ERO, empirically showing promise in experience replay learning to improve the performance of off-policy reinforcement learning algorithms.

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

0 major / 3 minor

Summary. The manuscript introduces Experience Replay Optimization (ERO), a framework for off-policy RL that learns a replay policy to select experiences from a noisy, large replay buffer in order to maximize the agent's cumulative reward. The method alternates between (i) updating the agent policy on data sampled by the current replay policy and (ii) updating the replay policy to supply the experiences most useful to the agent. Experiments on continuous-control tasks are presented as empirical evidence that ERO improves performance relative to uniform or rule-based replay.

Significance. If the reported gains are reproducible and the alternating optimization is stable, the work supplies a concrete, learnable alternative to hand-designed replay heuristics. This is a direct attack on a long-standing practical bottleneck in off-policy methods; a successful instantiation would be of immediate interest to the continuous-control and robotics communities.

minor comments (3)
  1. The abstract states that the replay memory is 'noisy and large' and the cumulative reward is 'unstable,' yet the manuscript does not quantify these difficulties (e.g., variance of TD targets or buffer size relative to episode length) before claiming that the alternating scheme resolves them.
  2. Section 4 (experiments) should report the precise off-policy base algorithms, the number of random seeds, and whether the replay-policy parameters are shared across tasks or re-learned per environment.
  3. The notation distinguishing the agent policy π and the replay policy ρ is introduced only in the method section; a short table of symbols at the beginning would improve readability.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. We are pleased that the potential of ERO as a learnable alternative to hand-designed replay heuristics is recognized.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The provided abstract and description present ERO as an alternating-update framework for learning a replay policy alongside the agent policy, with effectiveness shown via experiments on continuous control tasks. No equations, parameter fits, self-citations, or uniqueness theorems appear in the text that would reduce any claimed prediction or result to its own inputs by construction. The load-bearing elements are the empirical demonstrations and the stated handling of noisy replay memory, both of which are externally falsifiable and independent of the method's internal definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated. The central claim rests on the unstated assumption that the replay policy can be optimized despite noisy memory and unstable reward signals.

pith-pipeline@v0.9.0 · 5681 in / 1003 out tokens · 16593 ms · 2026-05-25T20:01:56.005013+00:00 · methodology

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