RANDOMIZED ENTITY-WISE FACTORIZATION FOR MULTI-AGENT REINFORCEMENT LEARNING

Abstract

Real world multi-agent tasks often involve varying types and quantities of agents and non-agent entities; however, agents within these tasks rarely need to consider all others at all times in order to act effectively. Factored value function approaches have historically leveraged such independences to improve learning efficiency, but these approaches typically rely on domain knowledge to select fixed subsets of state features to include in each factor. We propose to utilize value function factoring with random subsets of entities in each factor as an auxiliary objective in order to disentangle value predictions from irrelevant entities. This factoring approach is instantiated through a simple attention mechanism masking procedure. We hypothesize that such an approach helps agents learn more effectively in multi-agent settings by discovering common trajectories across episodes within sub-groups of agents/entities. Our approach, Randomized Entity-wise Factorization for Imagined Learning (REFIL), outperforms all strong baselines by a significant margin in challenging StarCraft micromanagement tasks.

1. INTRODUCTION

Figure 1 : Breakaway sub-scenario in soccer. Agents in the yellow square can ignore the context outside of this region and still predict their success effectively. Many real-world multi-agent tasks contain scenarios in which an agent must deal with varying numbers and/or types of cooperative agents, antagonist enemies or other entities. Agents, however, can often select their optimal actions while ignoring a subset of agents/entities. For example, in the sport of soccer, a "breakaway" occurs when an attacker with the ball passes the defense and only needs to beat the goalkeeper in order to score (see Figure 1 ). In this situation, only the opposing goalkeeper is immediately relevant to the attacker's success, so the attacker can safely ignore players other than the goalkeeper for the time being. By ignoring irrelevant context, the attacker can generalize this experience better to its next breakaway. Furthermore, soccer takes many forms, from casual 5 vs. 5 to full scale 11 vs. 11 matches, and breakaways occur in all. If agents can identify independent patterns of behavior such as breakaways, they should be able to learn more efficiently as well as share their experiences across all forms of soccer. Value function factoring approaches attempt to leverage independences between agents, such as those in our soccer example, by learning value functions as a combination of independent factors that depend on disjunct subsets of the state and action spaces (Koller & Parr, 1999) . These subsets are typically fixed in advance using domain knowledge about the problem at hand, and thus are not scalable to complex domains where dependencies are unknown and may shift over time. Recent approaches in cooperative deep multi-agent reinforcement learning (MARL) factor value functions into separate components for each agent's action and observation space in order to enable decentralized execution (e.g., VDN (Sunehag et al., 2018 ), QMIX (Rashid et al., 2018) ). These approaches learn a utility function for each agent that only depends on the agent's own action and its observations. The global Q-value is then predicted as some monotonic combination of these utilities in order to allow agents to greedily select their actions with local information while maximizing the 1

