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Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning

Jihwan Oh, Sungnyun Kim, Gahee Kim, Sunghwan Kim, Se-Young Yun

TL;DR

This work addresses data scarcity in offline multi-agent reinforcement learning by introducing EAQ, a diffusion-basedEpisodes Augmentation method guided by the total Q-value. EAQ composes offline MARL data via a Conv1D diffusion model that learns joint agent observations, actions, rewards, and termination signals, while implicitly steering generated episodes toward cooperativeness through $Q^{\text{tot}}$ guidance. By training a single diffusion model and incorporating $Q^{\text{tot}}$-based loss, EAQ simplifies architecture and improves normalized returns on SMAC benchmarks, notably +17.3% for medium policies and +12.9% for poor policies, compared to the original dataset. The results also show EAQ$^{-Q}$ can still yield gains, and effectiveness depends on the quality of the baseline dataset, indicating room for broader applicability and future work on continuous actions and alternative generative models.

Abstract

Offline multi-agent reinforcement learning (MARL) is increasingly recognized as crucial for effectively deploying RL algorithms in environments where real-time interaction is impractical, risky, or costly. In the offline setting, learning from a static dataset of past interactions allows for the development of robust and safe policies without the need for live data collection, which can be fraught with challenges. Building on this foundational importance, we present EAQ, Episodes Augmentation guided by Q-total loss, a novel approach for offline MARL framework utilizing diffusion models. EAQ integrates the Q-total function directly into the diffusion model as a guidance to maximize the global returns in an episode, eliminating the need for separate training. Our focus primarily lies on cooperative scenarios, where agents are required to act collectively towards achieving a shared goal-essentially, maximizing global returns. Consequently, we demonstrate that our episodes augmentation in a collaborative manner significantly boosts offline MARL algorithm compared to the original dataset, improving the normalized return by +17.3% and +12.9% for medium and poor behavioral policies in SMAC simulator, respectively.

Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning

TL;DR

This work addresses data scarcity in offline multi-agent reinforcement learning by introducing EAQ, a diffusion-basedEpisodes Augmentation method guided by the total Q-value. EAQ composes offline MARL data via a Conv1D diffusion model that learns joint agent observations, actions, rewards, and termination signals, while implicitly steering generated episodes toward cooperativeness through guidance. By training a single diffusion model and incorporating -based loss, EAQ simplifies architecture and improves normalized returns on SMAC benchmarks, notably +17.3% for medium policies and +12.9% for poor policies, compared to the original dataset. The results also show EAQ can still yield gains, and effectiveness depends on the quality of the baseline dataset, indicating room for broader applicability and future work on continuous actions and alternative generative models.

Abstract

Offline multi-agent reinforcement learning (MARL) is increasingly recognized as crucial for effectively deploying RL algorithms in environments where real-time interaction is impractical, risky, or costly. In the offline setting, learning from a static dataset of past interactions allows for the development of robust and safe policies without the need for live data collection, which can be fraught with challenges. Building on this foundational importance, we present EAQ, Episodes Augmentation guided by Q-total loss, a novel approach for offline MARL framework utilizing diffusion models. EAQ integrates the Q-total function directly into the diffusion model as a guidance to maximize the global returns in an episode, eliminating the need for separate training. Our focus primarily lies on cooperative scenarios, where agents are required to act collectively towards achieving a shared goal-essentially, maximizing global returns. Consequently, we demonstrate that our episodes augmentation in a collaborative manner significantly boosts offline MARL algorithm compared to the original dataset, improving the normalized return by +17.3% and +12.9% for medium and poor behavioral policies in SMAC simulator, respectively.
Paper Structure (22 sections, 5 equations, 3 figures, 1 table, 1 algorithm)

This paper contains 22 sections, 5 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Pioneering episodes augmentation for MARL:
  3. Cooperative trajectories augmentation:
  4. Simplification of model architecture:
  5. Preliminaries
  6. Offline multi-agent reinforcement learning
  7. Diffusion models
  8. Data synthesis in RL
  9. Synthesizing Multi-Agent Offline Data
  10. Integration of agents’ features.
  11. Enhancing episode cooperativeness.
  12. Training data preparation
  13. Implicit $Q^\text{tot}$ loss guidance augmentation
  14. Visualization of augmented dataset
  15. Experiments
  16. ...and 7 more sections

Figures (3)

  • Figure 1: (a) EAQ enhances the dataset to involve cooperative behaviors among agents. For instance, in a scenario like 3marines vs 3marines in StarCraft2, it encourages agents to sequentially target and attack a single enemy, which is a good strategy to win. (b) illustrates the proportion of focusing fire actions on a single enemy when all agents choose to attack. Compared to the original dataset, EAQ has successfully increased the frequency of cooperative behaviors, towards achieving a shared goal.
  • Figure 2: Our proposed algorithm architecture for MARL episodes augmentation. (a) represents the example of MARL offline dataset based on SMAC samvelyan2019starcraft environments at time step = $t$. There are two teams that have to kill each other to win the battle. Here, the actions are in the discrete space such as digit which is converted into one-hot encoding format. (b) represents (1) how our training datasets are prepared and look like, (2) how EAQ is trained under the guidance of $Q_{t}^{\text{tot}}$ and (3) how highly the values in the datasets is multi-modal.
  • Figure 3: We visualize a t-SNE JMLR:v9:vandermaaten08a projection on the observation space of original dataset and EAQ augmented dataset on several scenarios in SMAC environment.