RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm

• URL: http://arxiv.org/abs/2411.08392v1
• Date: Wed, 13 Nov 2024 07:24:14 GMT
• Title: RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm
• Authors: Geetansh Kalra, Divye Singh, Justin Jose,
• Abstract summary: Reinforcement Learning (RL) is a rapidly growing area of machine learning. Assessing RL models can be challenging, which makes it difficult to interpret their behaviour. We have developed RLInspect, an interactive visual analytic tool. It takes into account different components of the RL model - state, action, agent architecture and reward, and provides a more comprehensive view of the RL training.
• Score: 0.0
• License:
• Abstract: Reinforcement Learning (RL) is a rapidly growing area of machine learning that finds its application in a broad range of domains, from finance and healthcare to robotics and gaming. Compared to other machine learning techniques, RL agents learn from their own experiences using trial and error, and improve their performance over time. However, assessing RL models can be challenging, which makes it difficult to interpret their behaviour. While reward is a widely used metric to evaluate RL models, it may not always provide an accurate measure of training performance. In some cases, the reward may seem increasing while the model's performance is actually decreasing, leading to misleading conclusions about the effectiveness of the training. To overcome this limitation, we have developed RLInspect - an interactive visual analytic tool, that takes into account different components of the RL model - state, action, agent architecture and reward, and provides a more comprehensive view of the RL training. By using RLInspect, users can gain insights into the model's behaviour, identify issues during training, and potentially correct them effectively, leading to a more robust and reliable RL system.

Related papers

• Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think! [3.2031003471765285]
  reinforcement learning platforms often emphasise the design of RL algorithms and the training performance but neglect the correctness of models and reward functions. This paper proposes using formal methods to model autonomous driving systems and demonstrates how model checking (MC) can be used in RL for AD.
  arXiv  Detail & Related papers  (2024-11-21T18:09:20Z)
• The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning [8.36595587335589]
  Visual Reinforcement Learning methods often require extensive amounts of data. Model-based RL (MBRL) offers a potential solution with efficient data utilization through planning. MBRL lacks generalization capabilities for real-world tasks.
  arXiv  Detail & Related papers  (2024-11-15T13:21:26Z)
• Abstracted Trajectory Visualization for Explainability in Reinforcement Learning [2.1028463367241033]
  Explainable AI (XAI) has demonstrated the potential to help reinforcement learning (RL) practitioners to understand how RL models work. XAI for users who do not have RL expertise (non-RL experts) has not been studied sufficiently. We argue that abstracted trajectories, that depicts transitions between the major states of the RL model, will be useful for non-RL experts to build a mental model of the agents.
  arXiv  Detail & Related papers  (2024-02-05T21:17:44Z)
• Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning [69.19840497497503]
  It is argued that the commonly used action matching principle is more like an explanation of deep neural networks (DNNs) than the interpretation of RL agents. We propose to consider rewards, the essential objective of RL agents, as the essential objective of interpreting RL agents. We verify and evaluate our method on the Atari 2600 games as well as Duckietown, a challenging self-driving car simulator environment.
  arXiv  Detail & Related papers  (2023-09-04T09:09:54Z)
• A User Study on Explainable Online Reinforcement Learning for Adaptive Systems [0.802904964931021]
  Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty. Deep RL gaining interest, the learned knowledge is no longer explicitly represented, but is represented as a neural network. XRL-DINE provides visual insights into why certain decisions were made at important time points.
  arXiv  Detail & Related papers  (2023-07-09T05:12:42Z)
• Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning [93.99377042564919]
  This paper tries to build more flexible constraints for value estimation without impeding the exploration of potential advantages. The key idea is to leverage off-the-shelf RL simulators, which can be easily interacted with in an online manner, as the "test bed" for offline policies. We introduce CoWorld, a model-based RL approach that mitigates cross-domain discrepancies in state and reward spaces.
  arXiv  Detail & Related papers  (2023-05-24T15:45:35Z)
• 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)
• Light-weight probing of unsupervised representations for Reinforcement Learning [20.638410483549706]
  We study whether linear probing can be a proxy evaluation task for the quality of unsupervised RL representation. We show that the probing tasks are strongly rank correlated with the downstream RL performance on the Atari100k Benchmark. This provides a more efficient method for exploring the space of pretraining algorithms and identifying promising pretraining recipes.
  arXiv  Detail & Related papers  (2022-08-25T21:08:01Z)
• Contrastive Learning as Goal-Conditioned Reinforcement Learning [147.28638631734486]
  In reinforcement learning (RL), it is easier to solve a task if given a good representation. While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashion is unstable. We show (contrastive) representation learning methods can be cast as RL algorithms in their own right.
  arXiv  Detail & Related papers  (2022-06-15T14:34:15Z)
• RvS: What is Essential for Offline RL via Supervised Learning? [77.91045677562802]
  Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. In every environment suite we consider simply maximizing likelihood with two-layer feedforward is competitive. They also probe the limits of existing RvS methods, which are comparatively weak on random data.
  arXiv  Detail & Related papers  (2021-12-20T18:55:16Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)

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.