Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs

• URL: http://arxiv.org/abs/2406.19812v1
• Date: Fri, 28 Jun 2024 10:41:17 GMT
• Title: Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs
• Authors: Shiyu Zhang, Haoyang Song, Qixin Wang, Yu Pei,
• Abstract summary: We propose an automated oracle approach that leverages RL properties using fuzzy logic. Our oracle quantifies an agent's behavioral compliance with reward policies and analyzes its trend over training episodes. It labels an RL program as "Buggy" if the compliance trend violates expectations derived from RL characteristics.
• Score: 2.871991859211386
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reinforcement Learning (RL) has gained significant attention across various domains. However, the increasing complexity of RL programs presents testing challenges, particularly the oracle problem: defining the correctness of the RL program. Conventional human oracles struggle to cope with the complexity, leading to inefficiencies and potential unreliability in RL testing. To alleviate this problem, we propose an automated oracle approach that leverages RL properties using fuzzy logic. Our oracle quantifies an agent's behavioral compliance with reward policies and analyzes its trend over training episodes. It labels an RL program as "Buggy" if the compliance trend violates expectations derived from RL characteristics. We evaluate our oracle on RL programs with varying complexities and compare it with human oracles. Results show that while human oracles perform well in simpler testing scenarios, our fuzzy oracle demonstrates superior performance in complex environments. The proposed approach shows promise in addressing the oracle problem for RL testing, particularly in complex cases where manual testing falls short. It offers a potential solution to improve the efficiency, reliability, and scalability of RL program testing. This research takes a step towards automated testing of RL programs and highlights the potential of fuzzy logic-based oracles in tackling the oracle problem.

Related papers

• Necessary and Sufficient Oracles: Toward a Computational Taxonomy For Reinforcement Learning [28.184175745050474]
  We study the impact of the choice of supervised learning oracle on the computational complexity of reinforcement learning algorithms. First, we identify two-context regression as a minimal oracle in the standard episodic access model. Second, we identify one-context regression as a near-minimal oracle in the stronger reset access model. Third, we broaden our focus to Low-Rank MDPs, where we give cryptographic evidence that the analogous oracle from the Block MDP setting is insufficient.
  arXiv  Detail & Related papers  (2025-02-12T18:47:13Z)
• Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control [12.832009040635462]
  Reinforcement learning (RL) has proven to be well-performed and general-purpose in the inventory control (IC) domain. Online experience is expensive to acquire in real-world applications. Online experience may not reflect the true demand due to the lost sales phenomenon typical in IC.
  arXiv  Detail & Related papers  (2024-06-26T13:52:47Z)
• Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning [18.579378919155864]
  We propose Adaptive $Q$Network (AdaQN) to take into account the non-stationarity of the optimization procedure without requiring additional samples. AdaQN is theoretically sound and empirically validate it in MuJoCo control problems and Atari $2600 games.
  arXiv  Detail & Related papers  (2024-05-25T11:57:43Z)
• Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint [104.53687944498155]
  Reinforcement learning (RL) has been widely used in training large language models (LLMs) We propose a new RL method named RLMEC that incorporates a generative model as the reward model. Based on the generative reward model, we design the token-level RL objective for training and an imitation-based regularization for stabilizing RL process.
  arXiv  Detail & Related papers  (2024-01-11T17:58:41Z)
• Blending Imitation and Reinforcement Learning for Robust Policy Improvement [16.588397203235296]
  Imitation learning (IL) utilizes oracles to improve sample efficiency. RPI draws on the strengths of IL, using oracle queries to facilitate exploration. RPI is capable of learning from and improving upon a diverse set of black-box oracles.
  arXiv  Detail & Related papers  (2023-10-03T01:55:54Z)
• Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities [63.258517066104446]
  Reinforcement learning integrated as a component in the evolutionary algorithm has demonstrated superior performance in recent years. We discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets.
  arXiv  Detail & Related papers  (2023-08-25T15:06:05Z)
• A Survey of Meta-Reinforcement Learning [69.76165430793571]
  We cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.
  arXiv  Detail & Related papers  (2023-01-19T12:01:41Z)
• Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels [112.63440666617494]
  Reinforcement learning algorithms can succeed but require large amounts of interactions between the agent and the environment. We propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent. We show robust performance on the Real-Word RL benchmark, hinting at resiliency to environment perturbations during adaptation.
  arXiv  Detail & Related papers  (2022-09-24T14:22:29Z)
• Automated Reinforcement Learning (AutoRL): A Survey and Open Problems [92.73407630874841]
  Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL. We provide a common taxonomy, discuss each area in detail and pose open problems which would be of interest to researchers going forward.
  arXiv  Detail & Related papers  (2022-01-11T12:41:43Z)
• ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives [11.675763847424786]
  We present ShinRL, an open-source library for evaluation of reinforcement learning (RL) algorithms. ShinRL provides an RL environment interface that can compute metrics for delving into the behaviors of RL algorithms. We show how combining these two features of ShinRL makes it easier to analyze the behavior of deep Q learning.
  arXiv  Detail & Related papers  (2021-12-08T05:34:46Z)
• Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning [56.17667147101263]
  In real-world tasks, reinforcement learning agents encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations. We propose the Robust Hallucinated Upper-Confidence RL (RH-UCRL) algorithm to provably solve this problem.
  arXiv  Detail & Related papers  (2021-03-18T16:50:17Z)

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.