Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
Pith reviewed 2026-05-24 16:37 UTC · model grok-4.3
The pith
Adding a terminal prediction auxiliary task improves A3C performance across multiple reinforcement learning domains.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Terminal Prediction estimates how close the current state is to a terminal state in episodic tasks. When used as an auxiliary task alongside the main policy objective in A3C, it produces improved learning efficiency and higher-quality policies on Atari games, BipedalWalker, and Pommerman against varied opponents.
What carries the argument
Terminal Prediction (TP), an auxiliary task estimating temporal closeness to terminal states to supply a self-supervised representation-learning signal.
If this is right
- A3C-TP outperforms or matches standard A3C in most tested Atari domains.
- In Pommerman the method yields faster learning and better policies against different opponents.
- Performance is similar or better than baseline in the BipedalWalker domain.
- The auxiliary task can be added to other algorithms beyond A3C for episodic tasks.
Where Pith is reading between the lines
- Explicitly modeling time-to-termination may produce value estimates that better account for episode length in sparse-reward settings.
- The same auxiliary idea could be adapted to predict time until other salient events, such as goal achievement, in continuing tasks.
- Combining terminal prediction with existing auxiliary tasks like pixel reconstruction or reward prediction may yield additive gains in representation quality.
Load-bearing premise
Predicting the temporal distance to terminal states supplies a useful learning signal that improves the quality of representations used by the policy network.
What would settle it
If side-by-side runs of A3C-TP and standard A3C on the same Atari, BipedalWalker, and Pommerman setups show no consistent gain in learning speed or final return for the TP variant, the claimed benefit would not hold.
read the original abstract
Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency. In this paper, we contribute a novel self-supervised auxiliary task, i.e., Terminal Prediction (TP), estimating temporal closeness to terminal states for episodic tasks. The intuition is to help representation learning by letting the agent predict how close it is to a terminal state, while learning its control policy. Although TP could be integrated with multiple algorithms, this paper focuses on Asynchronous Advantage Actor-Critic (A3C) and demonstrating the advantages of A3C-TP. Our extensive evaluation includes: a set of Atari games, the BipedalWalker domain, and a mini version of the recently proposed multi-agent Pommerman game. Our results on Atari games and the BipedalWalker domain suggest that A3C-TP outperforms standard A3C in most of the tested domains and in others it has similar performance. In Pommerman, our proposed method provides significant improvement both in learning efficiency and converging to better policies against different opponents.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes Terminal Prediction (TP) as a self-supervised auxiliary task for episodic deep RL: the agent learns to predict its temporal distance to terminal states while optimizing its policy. The method is instantiated as A3C-TP and evaluated on a suite of Atari games, the BipedalWalker continuous-control domain, and a mini multi-agent Pommerman environment. The central empirical claim is that A3C-TP outperforms or matches standard A3C across most tested domains and yields statistically noticeable gains in both learning speed and final policy quality in Pommerman against varied opponents.
Significance. If the reported gains prove robust, the work supplies a lightweight, domain-agnostic auxiliary objective that can be added to existing actor-critic methods with minimal architectural change. It extends the auxiliary-task literature (e.g., UNREAL) by focusing explicitly on terminal proximity, which may improve representation quality for episodic tasks. The breadth of domains (discrete Atari, continuous locomotion, multi-agent) is a positive feature.
major comments (2)
- [Results section] Results section (and associated tables/figures): average scores are reported without error bars, number of independent seeds, or any statistical significance test. Because the headline claim is that A3C-TP “outperforms standard A3C in most of the tested domains,” the absence of these details makes it impossible to judge whether observed differences are reliable or could be explained by random seed variation.
- [§3] §3 (method) and experimental protocol: the weighting coefficient that balances the TP auxiliary loss against the A3C objective is introduced but no sensitivity analysis or default value is provided. If performance gains are confined to a narrow range of this hyper-parameter, the practical utility of the method is reduced.
minor comments (2)
- [Pommerman experiments] The abstract states “significant improvement” in Pommerman; the corresponding experimental subsection should explicitly define the evaluation metric (win rate, average return, etc.) and the set of opponents used.
- [§3] Notation for the temporal-distance target is introduced without a formal definition or pseudocode; adding a short algorithmic box would improve reproducibility.
Simulated Author's Rebuttal
We thank the referee for the positive assessment and constructive comments. We address each major comment below.
read point-by-point responses
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Referee: [Results section] Results section (and associated tables/figures): average scores are reported without error bars, number of independent seeds, or any statistical significance test. Because the headline claim is that A3C-TP “outperforms standard A3C in most of the tested domains,” the absence of these details makes it impossible to judge whether observed differences are reliable or could be explained by random seed variation.
Authors: We agree that the reported results would be strengthened by the inclusion of error bars, the number of independent seeds, and statistical significance tests. In the revised manuscript we will report results averaged over multiple independent seeds, add error bars to all figures and tables, and include statistical tests (e.g., paired t-tests) to support claims of outperformance where appropriate. revision: yes
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Referee: [§3] §3 (method) and experimental protocol: the weighting coefficient that balances the TP auxiliary loss against the A3C objective is introduced but no sensitivity analysis or default value is provided. If performance gains are confined to a narrow range of this hyper-parameter, the practical utility of the method is reduced.
Authors: We agree that explicitly stating the default value and providing a sensitivity analysis would improve clarity and demonstrate robustness. In the revised manuscript we will state the default value used for the weighting coefficient and add a sensitivity analysis on a representative subset of environments. revision: yes
Circularity Check
No significant circularity identified
full rationale
The manuscript is an empirical proposal introducing a terminal-prediction auxiliary loss for A3C. No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the argument structure. The central claim of improved performance rests on experimental comparisons across domains rather than any self-referential construction, satisfying the criteria for a self-contained empirical result.
discussion (0)
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