arxiv_ml 91% Match Research Paper Reinforcement learning researchers,Robotics engineers,AI researchers,Machine learning practitioners 20 hours ago

Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning

reinforcement-learning › robotics-rl

📄 Abstract

Abstract: We argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Reinforcement Learning (CRL) algorithms. Through comprehensive experimental validation, we demonstrate that such issue frequently exists in CRL and cannot be effectively addressed by several recent work on either mitigating plasticity loss of RL agents or enhancing the positive transfer in CRL scenario. To that end, we develop Reset & Distill (R&D), a simple yet highly effective baseline method, to overcome the negative transfer problem in CRL. R&D combines a strategy of resetting the agent's online actor and critic networks to learn a new task and an offline learning step for distilling the knowledge from the online actor and previous expert's action probabilities. We carried out extensive experiments on long sequence of Meta World tasks and show that our simple baseline method consistently outperforms recent approaches, achieving significantly higher success rates across a range of tasks. Our findings highlight the importance of considering negative transfer in CRL and emphasize the need for robust strategies like R&D to mitigate its detrimental effects.

Key Contributions

Addresses the critical problem of negative transfer in Continual Reinforcement Learning (CRL) with a novel method called Reset & Distill (R&D). R&D resets agent networks for new tasks and distills knowledge from previous experts, effectively mitigating negative transfer and enhancing learning efficiency.

Business Value

Enables AI agents, particularly robots, to learn new skills and adapt to changing environments over time without forgetting previous knowledge, leading to more versatile and adaptable autonomous systems.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Feasible for RL agents that need to learn sequentially. Requires careful tuning of the reset and distillation parameters.

Limitations Addressed

The negative transfer problem in Continual Reinforcement Learning, where learning a new task degrades performance on previous tasks or hinders learning of the new task.

Performance Gains

Consistently outperforms existing methods in overcoming negative transfer in CRL.

Technical Tags

continual reinforcement learningnegative transferplasticity lossknowledge distillationactor-criticmeta-worldreinforcement learningtask learning

Research Topics

Reinforcement LearningContinual LearningMachine LearningRoboticsArtificial Intelligence

Methods & Architectures

Reset & Distill (R&D)Resetting actor and critic networksOffline learningKnowledge distillation Actor-Critic networks

Applications & Tasks

Robotics Reinforcement Learning Artificial Intelligence Continual LearningTransfer LearningTask Adaptation Continual Reinforcement Learning (CRL)Learning new tasks sequentially

Datasets & Benchmarks

Datasets

Meta-World tasks

Benchmarks

Consistently outperforms other methods on long sequences of Meta-World tasks.

Related Fields

Machine LearningReinforcement LearningRoboticsArtificial IntelligenceContinual Learning

Keywords

continual reinforcement learningCRLnegative transferknowledge distillationreinforcement learningmeta-worldtask learningtransfer learningroboticsonline learningactor-critic

Academic Context

#Reinforcement Learning#Continual Learning#Machine Learning#Robotics#Artificial Intelligence

Commercial Potential

Potential Products

Adaptive robotic systemsPersonalized AI tutorsIntelligent agents that learn over time

Target Industries

RoboticsManufacturingLogisticsGamingEducation

Use Case Examples

A robot learning to assemble new products without forgetting previous assembly tasksAn AI agent adapting to new game levels or rulesPersonalized learning systems that continuously improve

Competitive Edge

Provides a simple yet highly effective baseline method to overcome negative transfer, potentially outperforming more complex methods designed for plasticity or positive transfer.

Resource Requirements

Compute Needs

Requires significant compute for training RL agents, especially on long sequences of tasks.

Data Requirements

Requires environments and task sequences suitable for continual learning experiments (e.g., Meta-World).

Deployment Constraints

Tuning the reset and distillation parameters can be challenging. Performance might vary across different task types.

Scalability

Scalability depends on the efficiency of the underlying RL algorithm and the number/complexity of sequential tasks.

Production Readiness

Maturity Level

Research

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