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Learn to Change the World: Multi-level Reinforcement Learning with Model-Changing Actions

reinforcement-learning › multi-agent

📄 Abstract

Abstract: Reinforcement learning usually assumes a given or sometimes even fixed environment in which an agent seeks an optimal policy to maximize its long-term discounted reward. In contrast, we consider agents that are not limited to passive adaptations: they instead have model-changing actions that actively modify the RL model of world dynamics itself. Reconfiguring the underlying transition processes can potentially increase the agents' rewards. Motivated by this setting, we introduce the multi-layer configurable time-varying Markov decision process (MCTVMDP). In an MCTVMDP, the lower-level MDP has a non-stationary transition function that is configurable through upper-level model-changing actions. The agent's objective consists of two parts: Optimize the configuration policies in the upper-level MDP and optimize the primitive action policies in the lower-level MDP to jointly improve its expected long-term reward.

Authors (4)

Ziqing Lu

Babak Hassibi

Lifeng Lai

Weiyu Xu

Submitted

October 16, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Introduces the Multi-layer Configurable Time-Varying Markov Decision Process (MCTVMDP) to model environments where agents can actively change the underlying dynamics. This allows agents to not only adapt to but also proactively modify their environment to maximize rewards, offering a new paradigm for intelligent agents in dynamic settings.

Business Value

Enables more sophisticated autonomous systems that can adapt to and even shape their operational environments, leading to improved efficiency and robustness in complex industrial or robotic applications.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Requires significant theoretical development and empirical validation, but the core concepts are applicable to simulated and potentially real-world control systems.

Limitations Addressed

The assumption of fixed or passively adapted environments in traditional RL, which limits agent capabilities in scenarios requiring active environmental modification.

Technical Tags

Reinforcement LearningMarkov Decision ProcessModel-Changing ActionsConfigurable TransitionsMulti-layer MDPUpper-level MDPLower-level MDPPolicy OptimizationNon-stationary EnvironmentsDynamic Environments

Research Topics

Reinforcement Learning TheoryAgent AdaptationEnvironmental DynamicsDecision Making Under UncertaintyControl Theory

Methods & Architectures

Multi-layer Configurable Time-Varying Markov Decision Process (MCTVMDP)Policy Optimization Multi-layer MDP

Applications & Tasks

Robotics Autonomous Systems Game AI Industrial Automation Sequential Decision MakingControl ProblemsAdaptation to Changing Environments Optimizing policies in non-stationary environmentsLearning to reconfigure environment dynamics

Related Fields

Control TheoryOperations ResearchArtificial IntelligenceMachine Learning

Keywords

Reinforcement LearningMarkov Decision ProcessModel Changing ActionsEnvironment DynamicsAdaptationControlOptimizationNon-stationaryConfigurableMulti-layer

Academic Context

#Reinforcement Learning Theory#Agent Adaptation#Environmental Dynamics#Decision Making Under Uncertainty#Control Theory

Commercial Potential

Potential Products

Adaptive control systemsIntelligent agents for dynamic environments

Target Industries

ManufacturingRoboticsAerospaceAutonomous Vehicles

Use Case Examples

Robots that can reconfigure their workspaceAutonomous agents that learn to optimize factory processes by changing machine settings

Competitive Edge

Extends traditional RL by allowing agents to influence environment dynamics, offering a more powerful approach for complex, adaptive control problems.

Resource Requirements

Compute Needs

Likely high, depending on the complexity of the MCTVMDP and the optimization algorithms used.

Data Requirements

Requires environments that can be modeled as MCTVMDPs, potentially simulated or real-world systems with configurable dynamics.

Deployment Constraints

Complexity of modeling and training; ensuring stability and safety of model-changing actions.

Scalability

Scalability depends on the complexity of the state-action space and the efficiency of the optimization algorithms.

Production Readiness

Maturity Level

Theoretical/Early Research

Time to Market

Long-term

Patent Potential

Moderate, for novel algorithms and system designs enabling model-changing actions.

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