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arxiv: 2006.04363 · v2 · submitted 2020-06-08 · 💻 cs.LG · cs.AI· stat.ML

Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models

Farzane Aminmansour , Taher Jafferjee , Ehsan Imani , Erin Talvitie , Micheal Bowling , Martha White This is my paper

Pith reviewed 2026-05-24 14:22 UTC · model grok-4.3

classification 💻 cs.LG cs.AIstat.ML
keywords Dyna planningvalue hallucinationpredecessor modelsmodel-based reinforcement learningmultistep updatesreinforcement learningsample efficiencymodel error robustness
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The pith

Multistep predecessor models in Dyna avoid updating real states from simulated values to prevent misleading action values under model error.

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

The paper claims that Dyna agents can fail when an imperfect model causes the value function to bootstrap real-state estimates from simulated states, producing hallucinated values that distort the learned policy. It introduces the Hallucinated Value Hypothesis to formalize this risk and examines a design space of four Dyna variants that differ in whether they simulate forward or backward and whether they perform one-step or multistep updates. Three of the variants update real states toward simulated states and are therefore exposed to the hypothesized failure mode. The fourth variant, multistep predecessor models, updates without bootstrapping real states from simulated ones and is therefore positioned as more robust to model inaccuracies.

Core claim

Updating the values of real states towards values of simulated states can result in misleading action values which adversely affect the control policy; the multistep predecessor variant avoids this by not updating real states toward simulated states, providing experimental evidence for the Hallucinated Value Hypothesis across the four Dyna design variants.

What carries the argument

The multistep predecessor model, which simulates environment dynamics backward over multiple steps so that updates do not bootstrap real-state values from simulated-state values.

If this is right

  • Dyna agents using the multistep predecessor approach remain effective even when the learned model contains small errors.
  • The other three design combinations remain vulnerable to policy distortion from hallucinated values.
  • Multistep updates paired with backward simulation provide a concrete way to retain the sample-efficiency gains of Dyna without the associated robustness penalty.
  • Model-based planning can be made more reliable by choosing update directions that isolate real experience from simulated experience.

Where Pith is reading between the lines

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

  • The same separation of real and simulated value updates could be applied to other model-based methods that mix planning with real experience.
  • If the hypothesis holds, improvements in model accuracy alone may be less critical than previously thought for Dyna-style algorithms.
  • The approach may extend to settings where the model is learned online and errors cannot be fully eliminated.

Load-bearing premise

Bootstrapping real-state values from simulated-state values is the primary cause of Dyna failure under model error rather than other forms of model inaccuracy or update mechanics.

What would settle it

An experiment in which the multistep predecessor variant still underperforms standard Dyna despite model error, or in which one of the three variants that updates real states from simulated states succeeds at the same rate.

Figures

Figures reproduced from arXiv: 2006.04363 by Ehsan Imani, Erin Talvitie, Farzane Aminmansour, Martha White, Micheal Bowling, Taher Jafferjee.

Figure 1
Figure 1. Figure 1: (a) Borderworld (b) An erroneous simulated transition We also see that these errors are persistent and do not resolve on their own. In the next section, we develop a design space of Dyna algorithms and discuss their implications with respect to the HVH. 4. A Design Space of Dyna Algorithms Dyna is a flexible framework which admits a variety of implementations. In this work we focus on two design choices th… view at source ↗
Figure 2
Figure 2. Figure 2: Visual comparison of Dyna algorithms. Circles and black arrows show a trajectory; solid circles are real states and dashed circles are simulated states. A red arrow means that the value of the origin state is updated towards the destination state. All algorithms except Multi-Step Predecessor Dyna allow updates towards hallucinated states. generate trajectories into the border and update values for real sta… view at source ↗
Figure 3
Figure 3. Figure 3: Learning curves on Borderworld. Only Multi-step Pre￾decessor Dyna & Uniterated One-step Predecessor Dyna succeed. Error bars are not visible as they are smaller than line thicknesses. environment model of Borderworld. Specifically, the model generates transitions from real states to hallucinated bor￾der states, as in Figure 1b (and from border states to bor￾der states). Further, we optimistically initialis… view at source ↗
Figure 4
Figure 4. Figure 4: Plot of maxa Q(s, a) ∀s ∈ S after 100, 000 steps. The red rectangles show where values of real states have been contami￾nated by values of simulated states. of real states – eventually they might no longer mislead the agent. However, this may take a long time and the agent will be catastrophically misled in the meantime. 5.2. Reinforcement Learning Benchmarks We now consider experiments that examine the HV… view at source ↗
Figure 5
Figure 5. Figure 5: Learning curves. The algorithms that update real state values to simulated state values stuggle while those that do not show robust performance. units to convergence using the DQN algorithm and froze its weights. In each step we input state st to the network and extracted the hidden layer activation to form a vector of state features φ(st). The value function was linear in φ(st). We initialised weights of … view at source ↗
Figure 6
Figure 6. Figure 6: Plot of maxa Q(s, a)∀s ∈ S for β = 0 (left) and β > 0 (right) after 2,000 steps. rithms converge to the optimal policy in about 5,000 real steps whereas Q-learning fails to reach that performance even after 20,000 steps. 6. The Impact of Model Iteration Multi-step Predecessor and Uniterated One-step Predeces￾sor are robust to hallucinated values. Which algorithm is preferred? Here, focus on β, a parameter … view at source ↗
Figure 7
Figure 7. Figure 7: Performance versus β. Multi-step Predecessor is the best performing algorithm. 7. Conclusion We presented the HVH: planning updates that move val￾ues of real states towards values of simulated states may propagate misleading, arbitrary value that impedes learning of control policies. Under controlled settings we showed evidence supporting the hypothesis — all algorithms that update real state values to sim… view at source ↗
read the original abstract

Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environment model. However, it is often difficult to learn accurate models of environment dynamics, and even small errors may result in failure of Dyna agents. In this paper, we highlight that one potential cause of that failure is bootstrapping off of the values of simulated states, and introduce a new Dyna algorithm to avoid this failure. We discuss a design space of Dyna algorithms, based on using successor or predecessor models -- simulating forwards or backwards -- and using one-step or multi-step updates. Three of the variants have been explored, but surprisingly the fourth variant has not: using predecessor models with multi-step updates. We present the \emph{Hallucinated Value Hypothesis} (HVH): updating the values of real states towards values of simulated states can result in misleading action values which adversely affect the control policy. We discuss and evaluate all four variants of Dyna amongst which three update real states toward simulated states -- so potentially toward hallucinated values -- and our proposed approach, which does not. The experimental results provide evidence for the HVH, and suggest that using predecessor models with multi-step updates is a promising direction toward developing Dyna algorithms that are more robust to model error.

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

3 major / 2 minor

Summary. The paper claims that a primary cause of Dyna-style RL failure under imperfect models is the 'Hallucinated Value Hypothesis' (HVH): bootstrapping real-state values from simulated-state values produces misleading action values that degrade the policy. It defines a 2x2 design space over successor vs. predecessor models and one-step vs. multi-step updates, observes that three of the four variants update real states toward simulated states, and introduces the previously unexplored multistep-predecessor variant that avoids this direction. The manuscript states that experiments across the four variants supply evidence for the HVH and that the new variant is a promising route to greater robustness.

Significance. If the HVH holds and the multistep-predecessor variant demonstrably outperforms the other three under realistic model error, the work supplies a lightweight algorithmic modification that could improve the reliability of Dyna-style planning without requiring more accurate models or additional regularization. The explicit enumeration of the four design-space cells is a clear organizational contribution.

major comments (3)
  1. [Abstract / Experiments] Abstract and experimental section: the statement that 'the experimental results provide evidence for the HVH' supplies no information on experimental design, number of runs, baselines, statistical tests, or effect sizes, so the support for the central claim cannot be assessed from the manuscript as written.
  2. [Design space / Experiments] Design-space argument (likely §3): the four combinations are separated solely by whether real states are updated toward simulated ones, but no analysis or controlled experiment quantifies the relative contribution of this update direction versus other error channels (biased rewards, incorrect transition probabilities, or rollout distribution shift) when the learned model is imperfect; this leaves the HVH's status as the dominant failure mode untested.
  3. [Introduction / HVH definition] HVH statement: the hypothesis is introduced as an ad-hoc explanatory construct without a formal derivation, bound, or falsifiable prediction that would distinguish it from generic model-error effects; the empirical comparison therefore carries the entire burden of proof.
minor comments (2)
  1. [Design space] Notation for the four variants is introduced informally; a compact table or diagram labeling each cell (successor/one-step, predecessor/multi-step, etc.) would improve readability.
  2. [Related work] The manuscript does not cite prior work on backward models or multi-step value iteration in the Dyna literature; adding these references would clarify novelty.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and note where the manuscript will be revised.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and experimental section: the statement that 'the experimental results provide evidence for the HVH' supplies no information on experimental design, number of runs, baselines, statistical tests, or effect sizes, so the support for the central claim cannot be assessed from the manuscript as written.

    Authors: We agree the abstract statement is too brief. The experimental section details the environments, number of independent runs with means and standard errors, and direct comparisons among variants. We will revise the abstract to qualify the claim or briefly indicate the nature of the supporting experiments. revision: yes

  2. Referee: [Design space / Experiments] Design-space argument (likely §3): the four combinations are separated solely by whether real states are updated toward simulated ones, but no analysis or controlled experiment quantifies the relative contribution of this update direction versus other error channels (biased rewards, incorrect transition probabilities, or rollout distribution shift) when the learned model is imperfect; this leaves the HVH's status as the dominant failure mode untested.

    Authors: All variants use the identical learned model, isolating the effect of update direction from other model errors. Performance differences are therefore attributable to whether real states are updated from simulated values. We will add explicit discussion of this controlled comparison. revision: partial

  3. Referee: [Introduction / HVH definition] HVH statement: the hypothesis is introduced as an ad-hoc explanatory construct without a formal derivation, bound, or falsifiable prediction that would distinguish it from generic model-error effects; the empirical comparison therefore carries the entire burden of proof.

    Authors: The HVH is derived from the design-space enumeration and yields the testable prediction that the multistep-predecessor variant avoids updating real states from simulated ones. The experiments directly test this prediction. No formal bound is supplied, but the hypothesis organizes the variants and is evaluated empirically. revision: no

Circularity Check

0 steps flagged

No significant circularity; algorithmic proposal and empirical test are self-contained.

full rationale

The paper defines a design space over successor/predecessor models and one-step/multi-step updates, then proposes the multistep-predecessor variant that avoids updating real-state values from simulated ones. The Hallucinated Value Hypothesis is stated as a claim about error propagation and is evaluated via experiments comparing the four variants. No equations, fitted parameters, or self-citations are used to derive the central claim; the benefit is demonstrated by direct comparison rather than by construction or renaming. This matches the default case of an independent algorithmic contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on standard RL assumptions about MDPs and value iteration plus the newly introduced Hallucinated Value Hypothesis; no free parameters or invented physical entities are described.

axioms (1)
  • standard math The environment can be modeled as a Markov decision process and value functions can be updated via bootstrapping.
    The entire Dyna framework and the four variants presuppose the standard MDP and temporal-difference update setting.
invented entities (1)
  • Hallucinated Value Hypothesis no independent evidence
    purpose: To explain a specific mechanism by which model error harms Dyna performance.
    The hypothesis is introduced in the paper as the load-bearing explanation for observed failures.

pith-pipeline@v0.9.0 · 5792 in / 1315 out tokens · 54930 ms · 2026-05-24T14:22:16.941958+00:00 · methodology

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