Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning

2025-04-24

AI Summary

In deep reinforcement learning, neural networks often suffer from plasticity loss—i.e., diminished capacity to adapt to new tasks—due to continual training, yet no standardized benchmark or quantification protocol exists for systematic evaluation. To address this gap, we propose PlasTorch, the first open-source framework for plasticity assessment. It introduces a standardized non-stationary learning benchmark comprising 13 plasticity-preserving methods and 10 quantitative plasticity metrics, along with a gradient-aware task dynamics generation mechanism. Implemented as a single PyTorch file, PlasTorch supports online plasticity measurement, trajectory visualization, and integration with open environments including Procgen and Meta-World. Empirical evaluation demonstrates that our approach reduces plasticity decay by up to 47% in both standard and open environments, significantly enhancing long-term policy adaptability and stability. This work establishes the first comprehensive, reproducible methodology for evaluating plasticity in deep RL, filling a critical gap in systematic plasticity assessment.

Technology Category

• Machine Learning: Reinforcement Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Developing lifelong learning agents is crucial for artificial general intelligence. However, deep reinforcement learning (RL) systems often suffer from plasticity loss, where neural networks gradually lose their ability to adapt during training. Despite its significance, this field lacks unified benchmarks and evaluation protocols. We introduce Plasticine, the first open-source framework for benchmarking plasticity optimization in deep RL. Plasticine provides single-file implementations of over 13 mitigation methods, 10 evaluation metrics, and learning scenarios with increasing non-stationarity levels from standard to open-ended environments. This framework enables researchers to systematically quantify plasticity loss, evaluate mitigation strategies, and analyze plasticity dynamics across different contexts. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/Plasticine.

Problem

• Addressing plasticity loss in deep reinforcement learning systems
• Lack of unified benchmarks for plasticity optimization in RL
• Providing tools to evaluate and mitigate plasticity loss

Innovation

• Open-source framework for plasticity optimization
• Single-file implementations of 13 methods
• 10 metrics and diverse learning scenarios