DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning
Pith reviewed 2026-05-23 18:20 UTC · model grok-4.3
The pith
DROP derives optimism and pessimism from control as inference to build a distributional critic whose central value improves actor policies.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
DROP is a novel model derived from control as inference that introduces regular optimism and pessimism; when combined with ensemble learning it estimates a distributional value function as critic, and the central value of that function improves the actor policy, yielding learning performance comparable to state-of-the-art algorithms on dynamic tasks where the heuristic model failed.
What carries the argument
The DROP algorithm, which derives regular optimism and pessimism via control as inference and uses ensemble learning to form a distributional critic whose central value drives policy updates.
If this is right
- The central value extracted from the distributional critic can be used directly to improve the actor policy.
- DROP exhibits high generality across multiple dynamic tasks.
- Properly derived optimism and pessimism can elicit contributions that the heuristic asymmetric learning-rate model could not achieve.
- The method reaches learning performance comparable to current state-of-the-art reinforcement learning algorithms.
Where Pith is reading between the lines
- The control-as-inference derivation may allow similar optimism and pessimism terms to be inserted into other actor-critic architectures.
- Ensemble methods appear necessary to stabilize the distributional critic built from these terms.
- The approach could be tested on environments with longer horizons to check whether the central-value policy improvement continues to scale.
Load-bearing premise
That optimism and pessimism derived from control as inference, when paired with ensemble learning, produce a distributional critic whose central value actually improves policy learning on the tested dynamic tasks.
What would settle it
If DROP fails to match state-of-the-art performance or performs no better than the heuristic model when evaluated on the same dynamic tasks, the central claim would be falsified.
read the original abstract
In reinforcement learning (RL), temporal difference (TD) error is known to be related to the firing rate of dopamine neurons. It has been observed that each dopamine neuron does not behave uniformly, but each responds to the TD error in an optimistic or pessimistic manner, interpreted as a kind of distributional RL. To explain such a biological data, a heuristic model has also been introduced with learning rates asymmetric for the positive and negative TD errors. However, this heuristic model is not theoretically-grounded and unknown whether it can work as a RL algorithm. This paper therefore introduces a novel theoretically-grounded model with optimism and pessimism, which is derived from control as inference. In combination with ensemble learning, a distributional value function as a critic is estimated from regularly introduced optimism and pessimism. Based on its central value, a policy in an actor is improved. This proposed algorithm, so-called DROP (distributional and regular optimism and pessimism), is compared on dynamic tasks. Although the heuristic model showed poor learning performance, DROP demonstrated excellent performance in all tasks with high generality. In addition, DROP achieved learning performance comparable to the state-of-the-art algorithms. In other words, it was suggested that DROP is a new model that can elicit the potential contributions of optimism and pessimism.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript proposes DROP, a reinforcement learning algorithm derived from control as inference. It combines regularly introduced optimism and pessimism with ensemble learning to estimate a distributional value function as critic; the central value of this critic is then used to improve the actor policy. The paper claims that, unlike a heuristic asymmetric learning-rate model, DROP exhibits excellent performance and high generality on dynamic tasks while matching state-of-the-art algorithms, thereby demonstrating the utility of optimism and pessimism.
Significance. If the control-as-inference derivation is non-circular and the reported empirical gains are reproducible, the work would supply a principled mechanism for injecting distributional structure into RL critics, directly motivated by biological observations of heterogeneous dopamine responses. This could strengthen the link between control-as-inference frameworks and practical distributional RL.
major comments (1)
- The provided manuscript consists solely of the abstract; no equations, derivation steps, implementation details, or experimental protocol are available. Consequently the central claim—that optimism and pessimism derived from control as inference, when combined with ensembles, yield a distributional critic whose central value produces measurable policy improvement—cannot be verified for internal consistency or for whether any quantity reduces to a fitted parameter.
Simulated Author's Rebuttal
We thank the referee for their careful reading and for highlighting the need for verifiable technical content. The provided manuscript is indeed limited to the abstract, which prevents direct verification of the derivation and implementation.
read point-by-point responses
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Referee: The provided manuscript consists solely of the abstract; no equations, derivation steps, implementation details, or experimental protocol are available. Consequently the central claim—that optimism and pessimism derived from control as inference, when combined with ensembles, yield a distributional critic whose central value produces measurable policy improvement—cannot be verified for internal consistency or for whether any quantity reduces to a fitted parameter.
Authors: The observation is correct: only the abstract was supplied, so the control-as-inference derivation, ensemble construction of the distributional critic, and experimental protocol cannot be inspected or checked for internal consistency from the given text. We agree that a complete manuscript must contain these elements for the central claim to be evaluable. revision: yes
- Derivation steps, equations, implementation details, and experimental protocol are absent from the provided manuscript (only the abstract is available), preventing any substantive verification or defense of the technical claims.
Circularity Check
No circularity detectable from abstract alone
full rationale
Only the abstract is provided, which states that the model 'is derived from control as inference' and combined with ensemble learning, but contains no equations, no self-citations, and no fitted parameters presented as predictions. No load-bearing step can be quoted or shown to reduce to its inputs by construction. The derivation is presented as external and theoretically grounded, with no evidence of self-definition, renaming, or imported uniqueness from the authors' prior work.
discussion (0)
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