Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2411.07700v1
• Date: Tue, 12 Nov 2024 10:26:44 GMT
• Title: Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning
• Authors: Stefan Pranger, Hana Chockler, Martin Tappler, Bettina Könighofer,
• Abstract summary: In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. We propose a novel model-based method to rigorously compute a ranking of state importance across the entire state space. We then focus our testing efforts on the highest-ranked states.
• Score: 7.0247398611254175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. Since RL policies are very complex, testing efforts should concentrate on states in which the agent's decisions have the highest impact on the expected outcome. In this paper, we propose a novel model-based method to rigorously compute a ranking of state importance across the entire state space. We then focus our testing efforts on the highest-ranked states. In this paper, we focus on testing for safety. However, the proposed methods can be easily adapted to test for performance. In each iteration, our testing framework computes optimistic and pessimistic safety estimates. These estimates provide lower and upper bounds on the expected outcomes of the policy execution across all modeled states in the state space. Our approach divides the state space into safe and unsafe regions upon convergence, providing clear insights into the policy's weaknesses. Two important properties characterize our approach. (1) Optimal Test-Case Selection: At any time in the testing process, our approach evaluates the policy in the states that are most critical for safety. (2) Guaranteed Safety: Our approach can provide formal verification guarantees over the entire state space by sampling only a fraction of the policy. Any safety properties assured by the pessimistic estimate are formally proven to hold for the policy. We provide a detailed evaluation of our framework on several examples, showing that our method discovers unsafe policy behavior with low testing effort.

Related papers

• Reusable Test Suites for Reinforcement Learning [1.5826476446078004]
  This work presents Multi-Policy Test Case Selection (MPTCS), a novel automated test suite selection method for RL environments.<n>MPTCS uses a set of policies to select a diverse collection of reusable policy-agnostic test cases that reveal typical flaws in the agents' behavior.<n>We assess the effectiveness of the difficulty score and how the method's effectiveness and cost depend on the number of policies in the set.
  arXiv  Detail & Related papers  (2025-08-29T12:10:05Z)
• Probabilistic Shielding for Safe Reinforcement Learning [51.35559820893218]
  In real-life scenarios, a Reinforcement Learning (RL) agent must often also behave in a safe manner, including at training time. We present a new, scalable method, which enjoys strict formal guarantees for Safe RL. We show that our approach provides a strict formal safety guarantee that the agent stays safe at training and test time.
  arXiv  Detail & Related papers  (2025-03-09T17:54:33Z)
• Decision-Point Guided Safe Policy Improvement [22.885394395400592]
  Decision Points RL (DPRL) is an algorithm that restricts the set of state-action pairs (or regions for continuous states) considered for improvement. DPRL ensures high-confidence improvement in densely visited states while still utilizing data from sparsely visited states.
  arXiv  Detail & Related papers  (2024-10-12T04:05:56Z)
• CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies [30.57323631122579]
  We focus on threshold policies, a ubiquitous class of policies with applications in economics, healthcare, and digital advertising. Existing methods rely on potentially underpowered safety checks and limit the opportunities for finding safe improvements. We show that in adversarial settings, our approach controls the rate of adopting a policy worse than the baseline to the pre-specified error level.
  arXiv  Detail & Related papers  (2024-08-21T21:38:03Z)
• Learning Optimal Deterministic Policies with Stochastic Policy Gradients [62.81324245896716]
  Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. In common practice, convergence (hyper)policies are learned only to deploy their deterministic version. We show how to tune the exploration level used for learning to optimize the trade-off between the sample complexity and the performance of the deployed deterministic policy.
  arXiv  Detail & Related papers  (2024-05-03T16:45:15Z)
• Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation [4.837737516460689]
  We study the problem of conservative exploration, where the learner must at least be able to guarantee its performance is at least as good as a baseline policy. We propose the first conservative provably efficient model-free algorithm for policy optimization in continuous finite-horizon problems.
  arXiv  Detail & Related papers  (2023-12-24T10:59:32Z)
• Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery [13.333197887318168]
  Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. We propose a method to construct a boundary that discriminates safe and unsafe states. Our approach has better task performance with less safety violations than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-06-24T12:02:50Z)
• Safe Reinforcement Learning via Confidence-Based Filters [78.39359694273575]
  We develop a control-theoretic approach for certifying state safety constraints for nominal policies learned via standard reinforcement learning techniques. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-04T11:43:23Z)
• Supervised Off-Policy Ranking [145.3039527243585]
  Off-policy evaluation (OPE) leverages data generated by other policies to evaluate a target policy. We propose supervised off-policy ranking that learns a policy scoring model by correctly ranking training policies with known performance. Our method outperforms strong baseline OPE methods in terms of both rank correlation and performance gap between the truly best and the best of the ranked top three policies.
  arXiv  Detail & Related papers  (2021-07-03T07:01:23Z)
• Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety [1.9573380763700712]
  We introduce the feasible actor-critic (FAC) algorithm, which is the first model-free constrained safe reinforcement learning method. We claim that some states are inherently unsafe no matter what policy we choose, while for other states there exist policies ensuring safety, where we say such states and policies are feasible. We provide theoretical guarantees that FAC outperforms previous expectation-based constrained RL methods in terms of both constraint satisfaction and reward optimization.
  arXiv  Detail & Related papers  (2021-05-22T10:40:58Z)
• Reliable Off-policy Evaluation for Reinforcement Learning [53.486680020852724]
  In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy. We propose a novel framework that provides robust and optimistic cumulative reward estimates using one or multiple logged data.
  arXiv  Detail & Related papers  (2020-11-08T23:16:19Z)
• Conservative Safety Critics for Exploration [120.73241848565449]
  We study the problem of safe exploration in reinforcement learning (RL) We learn a conservative safety estimate of environment states through a critic. We show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates.
  arXiv  Detail & Related papers  (2020-10-27T17:54:25Z)
• Provably Good Batch Reinforcement Learning Without Great Exploration [51.51462608429621]
  Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Recent algorithms have shown promise but can still be overly optimistic in their expected outcomes. We show that a small modification to Bellman optimality and evaluation back-up to take a more conservative update can have much stronger guarantees.
  arXiv  Detail & Related papers  (2020-07-16T09:25:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.