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Rejecting Hallucinated State Targets during Planning (2410.07096v8)

Published 9 Oct 2024 in cs.AI

Abstract: Generative models can be used in planning to propose targets corresponding to states that agents deem either likely or advantageous to experience. However, imperfections, common in learned models, lead to infeasible hallucinated targets, which can cause delusional behaviors and thus safety concerns. This work first categorizes and investigates the properties of several kinds of infeasible targets. Then, we devise a strategy to reject infeasible targets with a generic target evaluator, which trains alongside planning agents as an add-on without the need to change the behavior nor the architectures of the agent (and the generative model) it is attached to. We highlight that, without proper design, the evaluator can produce delusional estimates, rendering the strategy futile. Thus, to learn correct evaluations of infeasible targets, we propose to use a combination of learning rule, architecture, and two assistive hindsight relabeling strategies. Our experiments validate significant reductions in delusional behaviors and performance improvements for several kinds of existing planning agents.

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Summary

  • The paper identifies various delusion types in target-directed RL agents, detailing generator and estimator errors that affect decision-making.
  • It introduces innovative hindsight relabeling and hybrid strategies to mitigate false beliefs and enhance the diversity of training data.
  • Experimental results show improved out-of-distribution generalization and estimation accuracy, advancing reliable decision-time planning in RL.

Insights on "Identifying and Addressing Delusions for Target-Directed Decision Making"

This paper presents a meticulous paper on the phenomenon of "delusions" in target-directed reinforcement learning (RL) agents. The authors address crucial aspects of how RL agents can develop false beliefs during decision-time planning, adversely affecting their performance, especially in out-of-distribution (OOD) scenarios. Through this work, they identify different types of delusions and propose strategies to mitigate these issues using hindsight relabeling techniques.

Key Contributions

  1. Identification of Delusions:
    • The paper classifies delusions into ones originating from generators and estimators within the RL framework. These include nonexistent targets (Type delusion:g1) and temporarily unreachable targets (Type delusion:g2).
    • Estimator delusions arise due to misevaluations, further categorized into types such as delusion:e0 (misevaluation of non-delusional targets), delusion:e1 (misjudgment of delusion:g1 targets), and delusion:e2 (misjudgment of delusion:g2 targets).
  2. Hindsight Relabeling Strategies:
    • The authors propose new hindsight relabeling strategies such as 0.4, 0.4, 0.4, and 0.7, 0.0, 0.0, aimed at improving the diversity of training data and addressing different types of delusions.
    • Hybrid strategies are formulated by combining various strategies to better train both the generator and estimator components of target-directed agents.
  3. Experimental Validation:
    • The paper conducts comprehensive experiments on custom environments like and to evaluate the effectiveness of the proposed strategies.
    • Stronger OOD generalization is observed for agents trained using the hybrid strategies, showcasing reduced delusional behaviors and improved estimation accuracy.

Implications and Future Work

The findings hold significant implications in advancing the reliability and applicability of RL agents in real-world scenarios where OOD situations are common. By identifying and addressing delusions, the paper proposes a pathway to improve the adaptive reasoning abilities of RL agents, allowing them to navigate evolving and unforeseen environments better.

Future research may explore other unexplored causes of delusions and further refine mitigation strategies to enhance the robustness of decision-time planning in RL. Furthermore, the general approach of ensuring diversity in training samples can be extended even to non-target-directed frameworks, offering a broader impact on the field of artificial intelligence.

Conclusion

This work contributes a nuanced understanding of the failure modes in target-directed RL frameworks and addresses them with well-formulated strategies. The meticulous breakdown of delusions and practical insights into alleviating them marks a step forward in improving RL agent performance, particularly in challenging OOD contexts.

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