ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
Yizhao Jin, Greg Slabaugh, Simon Lucas
TL;DR
This paper introduces ADAPTER-RL, a novel reinforcement learning adaptation method using adapters to improve training efficiency and adaptability of agents, tested in a real-time strategy game environment.
Contribution
It presents a new adapter-based strategy for reinforcement learning that enhances training efficiency and supports integration with pre-trained and rule-based agents.
Findings
Improved training efficiency in the nanoRTS environment.
Effective adaptation of pre-trained and rule-based agents.
Enhanced performance over baseline agents.
Abstract
Deep Reinforcement Learning (DRL) agents frequently face challenges in adapting to tasks outside their training distribution, including issues with over-fitting, catastrophic forgetting and sample inefficiency. Although the application of adapters has proven effective in supervised learning contexts such as natural language processing and computer vision, their potential within the DRL domain remains largely unexplored. This paper delves into the integration of adapters in reinforcement learning, presenting an innovative adaptation strategy that demonstrates enhanced training efficiency and improvement of the base-agent, experimentally in the nanoRTS environment, a real-time strategy (RTS) game simulation. Our proposed universal approach is not only compatible with pre-trained neural networks but also with rule-based agents, offering a means to integrate human expertise.
Peer Reviews
Decision·ICLR 2024 Conference Withdrawn Submission
The method seems reasonable.
* My main concern with this paper, and why I’m voting for rejection, is that it does not have a related work section that compares other adaptation techniques in RL, and does not compare the method introduced with baselines from prior work. It is hard to gauge the merits if this work without proper discussion of related work (specifically, adaptation methods in RL). * The text in the plots in Figure 3 is unreadable. * Typos: * Top of 2.3 “Which is a actor-critic paradigm” (PPO is not a para
(1) Interesting topic: Adaptation is an interesting and motivating area for reinforcement learning.
(1) Lack of related works discussion. The authors state that adaptation for RL is a largely unexplored area, while I would recommend doing a further literature review on similar topics. Some examples contain [1, 2, 3] (2) Insufficient experiments. The provided experiment results are limited. I would recommend doing more experiments with diverse tasks. For example, if the domain is focused on Reinforcement learning, you may try other tasks in the OpenAI gym [4]. (3) Lack of README file to run t
- The idea of using adapters for transfer in RL could be beneficial for sample efficiency and overcoming catastrophic forgetting. - Adapters provide a modular approach for incremental learning without interfering with the base agent. - Method can work with any base agent including rule-based and neural network agents.
1. The novelty is limited. Adapters have been widely studied for transfer learning in NLP and computer vision. Their application to RL is straightforward with no new techniques proposed. 2. There is no analysis on the learned adapter parameters to provide insights into how adaptation is occurring. Visualizations or other analysis would strengthen the approach. 3. The experimental evaluation is weak. Testing is limited to a simple RTS game with no comparisons to other multi-task or transfer RL te
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Taxonomy
TopicsReinforcement Learning in Robotics · Data Stream Mining Techniques