Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics

• URL: http://arxiv.org/abs/2406.14324v2
• Date: Wed, 05 Feb 2025 11:03:05 GMT
• Title: Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics
• Authors: Charlotte Beylier, Simon M. Hofmann, Nico Scherf,
• Abstract summary: We introduce attention-oriented metrics (ATOMs) to investigate the development of an RL agent's attention during training.<n>ATOMs successfully delineate the attention patterns of an agent trained on each game variation, and that these differences in attention patterns translate into differences in the agent's behaviour.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The learning process of a reinforcement learning (RL) agent remains poorly understood beyond the mathematical formulation of its learning algorithm. To address this gap, we introduce attention-oriented metrics (ATOMs) to investigate the development of an RL agent's attention during training. In a controlled experiment, we tested ATOMs on three variations of a Pong game, each designed to teach the agent distinct behaviours, complemented by a behavioural assessment. ATOMs successfully delineate the attention patterns of an agent trained on each game variation, and that these differences in attention patterns translate into differences in the agent's behaviour. Through continuous monitoring of ATOMs during training, we observed that the agent's attention developed in phases, and that these phases were consistent across game variations. Overall, we believe that ATOM could help improve our understanding of the learning processes of RL agents and better understand the relationship between attention and learning.

Related papers

• Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning [4.662814261930481]
  We introduce a scientific methodology for analyzing the learning process through quantitative analysis of saliency.<n>This approach aggregates saliency information at the object and modality level into hierarchical attention profiles.<n>This methodology uncovers algorithm-specific attention biases, reveals unintended reward-driven strategies, and diagnoses overfitting to redundant sensory channels.
  arXiv  Detail & Related papers  (2025-11-25T18:20:42Z)
• Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes [1.6874375111244329]
  Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers.<n>This work provides a data-driven understanding of the factors of human observers' understanding of the agent's learning process.
  arXiv  Detail & Related papers  (2025-06-16T15:04:27Z)
• Truly Self-Improving Agents Require Intrinsic Metacognitive Learning [59.60803539959191]
  Self-improving agents aim to continuously acquire new capabilities with minimal supervision.<n>Current approaches face two key limitations: their self-improvement processes are often rigid, fail to generalize across tasks domains, and struggle to scale with increasing agent capabilities.<n>We argue that effective self-improvement requires intrinsic metacognitive learning, defined as an agent's intrinsic ability to actively evaluate, reflect on, and adapt its own learning processes.
  arXiv  Detail & Related papers  (2025-06-05T14:53:35Z)
• Interpretable Learning Dynamics in Unsupervised Reinforcement Learning [0.10832949790701804]
  We present an interpretability framework for unsupervised reinforcement learning (URL) agents.<n>We analyze five agents DQN, RND, ICM, PPO, and a Transformer-RND variant trained on procedurally generated environments.
  arXiv  Detail & Related papers  (2025-05-06T19:57:09Z)
• Inverse Attention Agent for Multi-Agent System [6.196239958087161]
  A major challenge for Multi-Agent Systems is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. We introduce Inverse Attention Agents that adopt concepts from the Theory of Mind, implemented algorithmically using an attention mechanism and trained in an end-to-end manner. We demonstrate that the inverse attention network successfully infers the attention of other agents, and that this information improves agent performance.
  arXiv  Detail & Related papers  (2024-10-29T06:59:11Z)
• Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement [50.481380478458945]
  Iterative step-level Process Refinement (IPR) framework provides detailed step-by-step guidance to enhance agent training. Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.
  arXiv  Detail & Related papers  (2024-06-17T03:29:13Z)
• Empowering Large Language Model Agents through Action Learning [85.39581419680755]
  Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error. We argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents. We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions.
  arXiv  Detail & Related papers  (2024-02-24T13:13:04Z)
• Behavioral Analysis of Vision-and-Language Navigation Agents [21.31684388423088]
  Vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on surroundings. We develop a methodology to study agent behavior on a skill-specific basis.
  arXiv  Detail & Related papers  (2023-07-20T11:42:24Z)
• MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning [128.19212716007794]
  We propose an effective framework called textbfMulti-textbfAgent textbfMasked textbfAttentive textbfContrastive textbfLearning (MA2CL) MA2CL encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. Our method significantly improves the performance and sample efficiency of different MARL algorithms and outperforms other methods in various vision-based and state-based scenarios.
  arXiv  Detail & Related papers  (2023-06-03T05:32:19Z)
• Causality Detection for Efficient Multi-Agent Reinforcement Learning [0.0]
  We show how causality can be used to penalise lazy agents and improve their behaviours. We show empirically that using causality estimations in Multi-Agent Reinforcement Learning improves not only the holistic performance of the team, but also the individual capabilities of each agent.
  arXiv  Detail & Related papers  (2023-03-24T18:47:44Z)
• Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis [14.656957226255628]
  We introduce a model-agnostic method for discovery of behavior clusters in multiagent domains. Our framework makes no assumption about agents' underlying learning algorithms, does not require access to their latent states or models, and can be trained using entirely offline observational data.
  arXiv  Detail & Related papers  (2022-06-17T23:07:33Z)
• Learning Dynamics and Generalization in Reinforcement Learning [59.530058000689884]
  We show theoretically that temporal difference learning encourages agents to fit non-smooth components of the value function early in training. We show that neural networks trained using temporal difference algorithms on dense reward tasks exhibit weaker generalization between states than randomly networks and gradient networks trained with policy methods.
  arXiv  Detail & Related papers  (2022-06-05T08:49:16Z)
• 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)
• What is Going on Inside Recurrent Meta Reinforcement Learning Agents? [63.58053355357644]
  Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of "learning a learning algorithm" We shed light on the internal working mechanisms of these agents by reformulating the meta-RL problem using the Partially Observable Markov Decision Process (POMDP) framework.
  arXiv  Detail & Related papers  (2021-04-29T20:34:39Z)
• Joint Attention for Multi-Agent Coordination and Social Learning [108.31232213078597]
  We show that joint attention can be useful as a mechanism for improving multi-agent coordination and social learning. Joint attention leads to higher performance than a competitive centralized critic baseline across multiple environments. Taken together, these findings suggest that joint attention may be a useful inductive bias for multi-agent learning.
  arXiv  Detail & Related papers  (2021-04-15T20:14:19Z)
• Bridging the Imitation Gap by Adaptive Insubordination [88.35564081175642]
  We show that when the teaching agent makes decisions with access to privileged information, this information is marginalized during imitation learning. We propose 'Adaptive Insubordination' (ADVISOR) to address this gap. ADVISOR dynamically weights imitation and reward-based reinforcement learning losses during training, enabling on-the-fly switching between imitation and exploration.
  arXiv  Detail & Related papers  (2020-07-23T17:59:57Z)

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.