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DreamerV3-XP: Optimizing exploration through uncertainty estimation

reinforcement-learning › robotics-rl

📄 Abstract

Abstract: We introduce DreamerV3-XP, an extension of DreamerV3 that improves exploration and learning efficiency. This includes (i) a prioritized replay buffer, scoring trajectories by return, reconstruction loss, and value error and (ii) an intrinsic reward based on disagreement over predicted environment rewards from an ensemble of world models. DreamerV3-XP is evaluated on a subset of Atari100k and DeepMind Control Visual Benchmark tasks, confirming the original DreamerV3 results and showing that our extensions lead to faster learning and lower dynamics model loss, particularly in sparse-reward settings.

Authors (4)

Lukas Bierling

Davide Pasero

Jan-Henrik Bertrand

Kiki Van Gerwen

Submitted

October 24, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

DreamerV3-XP introduces two novel extensions to the DreamerV3 architecture: a prioritized replay buffer that scores trajectories by return, reconstruction loss, and value error, and an intrinsic reward based on disagreement over predicted environment rewards from an ensemble of world models. These extensions significantly improve exploration and learning efficiency, particularly in sparse-reward settings, leading to faster learning and lower dynamics model loss.

Business Value

Enables more efficient training of RL agents for complex tasks, reducing the need for extensive data collection and potentially accelerating the development of autonomous systems in robotics and other fields.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, as it builds upon an existing successful architecture (DreamerV3) and focuses on improving core learning mechanisms.

Limitations Addressed

Inefficient exploration in sparse-reward environments and slow learning in model-based RL.

Performance Gains

Faster learning, lower dynamics model loss, improved performance in sparse-reward settings.

Technical Tags

prioritized replay bufferintrinsic rewardworld modelsensemble learningexploration strategieslearning efficiencysparse rewardsmodel-based RLuncertainty estimation

Research Topics

Reinforcement LearningExploration in RLModel-Based RLDeep LearningRobotics

Methods & Architectures

Prioritized replay bufferEnsemble of world modelsIntrinsic reward shapingValue errorReconstruction loss DreamerV3World Models

Applications & Tasks

Robotics Game Playing Control Systems Exploration in sparse reward environmentsLearning efficiencyDynamics modeling Robotic manipulationAutonomous controlLearning from limited data

Datasets & Benchmarks

Datasets

Atari100k, DeepMind Control Visual Benchmark

Learning efficiencyDynamics model loss

Related Fields

Machine LearningArtificial IntelligenceRoboticsControl Theory

Keywords

DreamerV3-XPReinforcement LearningExplorationUncertainty EstimationPrioritized ReplayIntrinsic RewardWorld ModelsModel-Based RLSparse RewardsLearning EfficiencyRoboticsDeepMind ControlAtari

Academic Context

#Reinforcement Learning#Exploration in RL#Model-Based RL#Deep Learning#Robotics

Companies & Organizations

Companies Mentioned

DeepMind

Commercial Potential

Potential Products

Autonomous robotsGame-playing AISimulation environments

Target Industries

RoboticsGamingAutomotiveLogistics

Use Case Examples

Training robots for complex manipulation tasksDeveloping AI agents for challenging gamesOptimizing control systems in dynamic environments

Competitive Edge

Improves upon existing model-based RL exploration techniques by integrating prioritized replay and ensemble-based uncertainty for more efficient learning.

Market Opportunity

Large, driven by the growing fields of AI and robotics.

Revenue Models

Indirectthrough enabling more capable AI products and services.

Resource Requirements

Compute Needs

Significant, typical for training deep RL models on complex benchmarks.

Data Requirements

Requires environments with potentially sparse rewards and diverse dynamics for effective exploration.

Deployment Constraints

May require careful tuning of hyperparameters for optimal performance in specific environments.

Scalability

The ensemble approach might increase computational cost, but the core algorithms are generally scalable.

Production Readiness

Maturity Level

Research

Time to Market

Medium to Long, depending on integration into specific robotic or AI systems.

Patent Potential

Low, focuses on algorithmic improvements rather than novel hardware or specific product implementations.

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