Social Influence as Intrinsic Motivation for Multi-Agent Deep
Reinforcement Learning
Natasha Jaques and Angeliki Lazaridou and Edward Hughes and Caglar Gulcehre and Pedro A. Ortega and DJ Strouse and Joel Z. Leibo and Nando de Freitas
arXiv e-Print archive - 2018 via arXiv
Keywords: cs.LG, cs.AI, cs.MA, stat.ML
First published: 2018/10/19 (5 years ago)
Abstract: We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions that it could have taken, and computes their effect on the behavior of other agents. Actions that lead to bigger changes in other agents' behavior are considered influential and are rewarded. We show that this is equivalent to rewarding agents for having high mutual information between their actions. Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols. The influence rewards for all agents can be computed in a decentralized way by enabling agents to learn a model of other agents using deep neural networks. In contrast, key previous works on emergent communication in the MARL setting were unable to learn diverse policies in a decentralized manner and had to resort to centralized training. Consequently, the influence reward opens up a window of new opportunities for research in this area.
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Natasha Jaques and Angeliki Lazaridou and Edward Hughes and Caglar Gulcehre and Pedro A. Ortega and DJ Strouse and Joel Z. Leibo and Nando de Freitas
arXiv e-Print archive - 2018 via arXiv
Keywords: cs.LG, cs.AI, cs.MA, stat.ML
First published: 2018/10/19 (5 years ago)
Abstract: We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions that it could have taken, and computes their effect on the behavior of other agents. Actions that lead to bigger changes in other agents' behavior are considered influential and are rewarded. We show that this is equivalent to rewarding agents for having high mutual information between their actions. Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols. The influence rewards for all agents can be computed in a decentralized way by enabling agents to learn a model of other agents using deep neural networks. In contrast, key previous works on emergent communication in the MARL setting were unable to learn diverse policies in a decentralized manner and had to resort to centralized training. Consequently, the influence reward opens up a window of new opportunities for research in this area.
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This paper builds very directly on the idea of “empowerment” as an intrinsic reward for RL agents. Where empowerment incentivizes agents to increase the amount of influence they’re able to have over the environment, “social influence,” this paper’s metric, is based on the degree which the actions of one agent influence the actions of other agents, within a multi-agent setting. The goals between the two frameworks are a little different. The notion of “empowerment” is built around a singular agent trying to figure out a short-term proxy for likelihood of long-term survival (which is a feedback point no individual wants to hit). By contrast, the problems that the authors of this paper seek to solve are more explicitly multi-agent coordination problems: prisoner’s dilemma-style situations where collective reward requires cooperation. However, they share a mathematical basis: the idea that an agent’s influence on some other element of its environment (be it the external state, or another agent’s actions) is well modeled by calculating the mutual information between its agents and that element. While this is initially a bit of an odd conceptual jump, it does make sense: if an action can give statistical information to help you predict an outcome, it’s likely (obviously not certain, but likely) that that action influenced that outcome. In a multi-agent problem, where cooperation and potentially even communication can help solve the task, being able to influence other agents amounts to “finding ways to make oneself useful to other agents”, because other agents aren’t going to change behavior based on your actions, or “listen” to your “messages” (in the experiment where a communication channel was available between agents) if these signals don’t help them achieve *their* goals. So, this incentive, to influence the behavior of other (self-interested) agents, amounts to a good proxy for incentivizing useful cooperation. Zooming in on the exact mathematical formulations (which differ slightly from, though they’re in a shared spirit with, the empowerment math): the agent’s (A’s) Causal Influence reward is calculated by taking a KL divergence between the action distribution of the other agent (B) conditional on the action A took, compared to other actions A might have taken. (see below. Connecting back to empowerment: Mutual Information is just the expected value of this quantity, taken over A’s action distribution). https://i.imgur.com/oxXCbdK.png One thing you may notice from the above equation is that, because we’re working in KL divergences, we expect agent A to have access to the full distribution of agent B’s policy conditional on A’s action, not just the action B actually took. We also require the ability to sample “counterfactuals,” i.e. what agent B would have done if agent A had done something differently. Between these two requirements. If we take a realistic model of two agents interacting with each other, in only one timeline, only having access to the external and not internal parameters of the other, it makes it clear that these quantities can’t be pulled from direct experience. Instead, they are calculated by using an internal model: each agent builds its own MOA (Model of Other Agents), where they build a predictive model of what an agent will do at a given time, conditional on the environment and the actions of all other agents. It’s this model that is used to sample the aforementioned counterfactuals, since that just involves passing in a different input. I’m not entirely sure, in each experiment, whether the MOAs are trained concurrent with agent policies, or in a separate prior step. https://i.imgur.com/dn2cBg4.png Testing on, again, Prisoner’s Dilemma style problems requiring agents to take risky collaborative actions, the authors did find higher performance using their method, compared to approaches where each agent just maximizes its own external reward (which, it should be said, does depend on other agents’ actions), with no explicit incentive towards collaboration. Interestingly, when they specifically tested giving agents access to a “communication channel” (the ability to output discrete signals or “words” visible to other agents), they found that it was able to train just as effectively with only an influence reward, as it was with both an influence and external reward. 
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