Exploration by Random Distribution Distillation

• URL: http://arxiv.org/abs/2505.11044v1
• Date: Fri, 16 May 2025 09:38:21 GMT
• Title: Exploration by Random Distribution Distillation
• Authors: Zhirui Fang, Kai Yang, Jian Tao, Jiafei Lyu, Lusong Li, Li Shen, Xiu Li,
• Abstract summary: We propose a novel method called textbfRandom textbfDistribution textbfDistillation (RDD)<n>RDD samples the output of a target network from a normal distribution.<n>We demonstrate that RDD effectively unifies both count-based and prediction-error approaches.
• Score: 28.675586715243437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploration remains a critical challenge in online reinforcement learning, as an agent must effectively explore unknown environments to achieve high returns. Currently, the main exploration algorithms are primarily count-based methods and curiosity-based methods, with prediction-error methods being a prominent example. In this paper, we propose a novel method called \textbf{R}andom \textbf{D}istribution \textbf{D}istillation (RDD), which samples the output of a target network from a normal distribution. RDD facilitates a more extensive exploration by explicitly treating the difference between the prediction network and the target network as an intrinsic reward. Furthermore, by introducing randomness into the output of the target network for a given state and modeling it as a sample from a normal distribution, intrinsic rewards are bounded by two key components: a pseudo-count term ensuring proper exploration decay and a discrepancy term accounting for predictor convergence. We demonstrate that RDD effectively unifies both count-based and prediction-error approaches. It retains the advantages of prediction-error methods in high-dimensional spaces, while also implementing an intrinsic reward decay mode akin to the pseudo-count method. In the experimental section, RDD is compared with more advanced methods in a series of environments. Both theoretical analysis and experimental results confirm the effectiveness of our approach in improving online exploration for reinforcement learning tasks.

Related papers

• Exploratory Diffusion Model for Unsupervised Reinforcement Learning [28.413426177336703]
  Unsupervised reinforcement learning (URL) aims to pre-train agents by exploring diverse states or skills in reward-free environments.<n>Existing methods design intrinsic rewards to model the explored data and encourage further exploration.<n>We propose the Exploratory Diffusion Model (ExDM), which leverages the strong expressive ability of diffusion models to fit the explored data.
  arXiv  Detail & Related papers  (2025-02-11T05:48:51Z)
• Random Latent Exploration for Deep Reinforcement Learning [71.88709402926415]
  We introduce Random Latent Exploration (RLE), a simple yet effective exploration strategy in reinforcement learning (RL)<n>On average, RLE outperforms noise-based methods, which perturb the agent's actions, and bonus-based exploration, which rewards the agent for attempting novel behaviors.<n>RLE is as simple as noise-based methods, as it avoids complex bonus calculations but retains the deep exploration benefits of bonus-based methods.
  arXiv  Detail & Related papers  (2024-07-18T17:55:22Z)
• Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning [50.84938730450622]
  We propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios. Our method can be extended to more applications with high-density features in output spaces, such as multiple-choice questions.
  arXiv  Detail & Related papers  (2024-05-22T22:22:25Z)
• Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration [15.463313629574111]
  This paper investigates how to achieve sample-efficient exploration in continuous control tasks. We introduce an RL algorithm that incorporates a predictive model and off-policy learning elements. We derive an intrinsic reward without incurring parameters overhead.
  arXiv  Detail & Related papers  (2024-03-31T11:39:11Z)
• Exploration and Anti-Exploration with Distributional Random Network Distillation [28.68459770494451]
  This paper highlights the "bonus inconsistency" issue within the Random Network Distillation (RND) algorithm. To address this issue, we introduce the Distributional RND (DRND), a derivative of the RND. Our method effectively mitigates the inconsistency issue without introducing significant computational overhead.
  arXiv  Detail & Related papers  (2024-01-18T06:32:53Z)
• Implicit Variational Inference for High-Dimensional Posteriors [7.924706533725115]
  In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors. Our approach introduces novel bounds for approximate inference using implicit distributions by locally linearising the neural sampler.
  arXiv  Detail & Related papers  (2023-10-10T14:06:56Z)
• Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning [64.8463574294237]
  We propose Rewarding Episodic Visitation Discrepancy (REVD) as an efficient and quantified exploration method. REVD provides intrinsic rewards by evaluating the R'enyi divergence-based visitation discrepancy between episodes. It is tested on PyBullet Robotics Environments and Atari games.
  arXiv  Detail & Related papers  (2022-09-19T08:42:46Z)
• Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss [5.614122064282257]
  We propose an effective algorithm for detecting out-of-distribution examples utilizing PEDCC-Loss. We mathematically analyze the nature of the confidence score output by the PEDCC (Predefined Evenly-Distribution Class Centroids) classifier. We then construct a more effective scoring function to distinguish in-distribution (ID) and out-of-distribution.
  arXiv  Detail & Related papers  (2022-04-10T11:47:29Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game [140.19656665344917]
  We study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. We tackle this problem under the context of function approximation, leveraging powerful function approximators. We establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
  arXiv  Detail & Related papers  (2021-10-19T07:26:33Z)
• Regressive Domain Adaptation for Unsupervised Keypoint Detection [67.2950306888855]
  Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. We present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Our method brings large improvement by 8% to 11% in terms of PCK on different datasets.
  arXiv  Detail & Related papers  (2021-03-10T16:45:22Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)

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.