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Title: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Paper content:
Learning when to communicate and doing that effectively is essential in multi-agent tasks.
Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.
In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.
IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.
Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.
Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.
Communication is an essential element of intelligence as it helps in learning from others experience, work better in teams and pass down knowledge.
In multi-agent settings, communication allows agents to cooperate towards common goals.
Particularly in partially observable environments, when the agents are observing different parts of the environment, they can share information and learnings from their observation through communication.Recently, there have been a lot of success in the field of reinforcement learning (RL) in playing Atari Games BID22 to playing Go BID28 , most of which have been limited to the single agent domain.
However, the number of systems and applications having multi-agents have been growing BID16 BID23 ; where size can be from a team of robots working in manufacturing plants to a network of self-driving cars.
Thus, it is crucial to successfully scale RL to multi-agent environments in order to build intelligent systems capable of higher productivity.
Furthermore, scenarios other than cooperative, namely semi-cooperative (or mixed) and competitive scenarios have not even been studied as extensively for multi-agent systems.The mixed scenarios can be compared to most of the real life scenarios as humans are cooperative but not fully-cooperative in nature.
Humans work towards their individual goals while cooperating with each other.
In competitive scenarios, agents are essentially competing with each other for better rewards.
In real life, humans always have an option to communicate but can choose when to actually communicate.
For example, in a sports match two teams which can communicate, can choose to not communicate at all (to prevent sharing strategies) or use dishonest signaling (to misdirect opponents) BID18 in order to optimize their own reward and handicap opponents; making it important to learn when to communicate.Teaching agents how to communicate makes it is unnecessary to hand code the communication protocol with expert knowledge BID29 BID14 .
While the content of communication is important, it is also important to know when to communicate either to increase scalability and performance or to increase competitive edge.
For example, a prey needs to learn when to communicate to avoid communicating its location with predators.
BID29 showed that agents communicating through a continuous vector are easier to train and have a higher information throughput than communication based on discrete symbols.
Their continuous communication is differentiable, so it can be trained efficiently with back-propagation.
However, their model assumes full-cooperation between agents and uses average global rewards.
This restricts the model from being used in mixed or competitive scenarios as full-cooperation involves sharing hidden states to everyone; exposing everything and leading to poor performance by all agents as shown by our results.
Furthermore, the average global reward for all agents makes the credit assignment problem even harder and difficult to scale as agents don't know their individual contributions in mixed or competitive scenarios where they want themselves to succeed before others.To solve above mentioned issues, we make the following contributions:1.
We propose Individualized Controlled Continuous Communication Model (IC3Net), in which each agent is trained with its individualized reward and can be applied to any scenario whether cooperative or not.
2. We empirically show that based on the given scenario-using the gating mechanism-our model can learn when to communicate.
The gating mechanism allows agents to block their communication; which is useful in competitive scenarios.
3. We conduct experiments on different scales in three chosen environments including StarCraft and show that IC3Net outperforms the baselines with performance gaps that increase with scale.
The results show that individual rewards converge faster and better than global rewards.
In this work, we introduced IC3Net which aims to solve multi-agent tasks in various cooperation settings by learning when to communicate.
Its continuous communication enables efficient training by backpropagation, while the discrete gating trained by reinforcement learning along with individual rewards allows it to be used in all scenarios and on larger scale.Through our experiments, we show that IC3Net performs well in cooperative, mixed or competitive settings and learns to communicate only when necessary.
Further, we show that agents learn to stop communication in competitive cases.
We show scalability of our network by further experiments.
In future, we would like to explore possibility of having multi-channel communication where agents can decide on which channel they want to put their information similar to communication groups but dynamic.
It would be interesting to provide agents a choice of whether to listen to communication from a channel or not.

assistant: text: Author proposes a new architecture for multi-agent reinforcement learning that uses several LSTM controllers with tied weights that transmit a continuous vector to each other