Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train.
While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup.
In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model.
We model the environment, as seen by each of the agents, using separate but related Markov decision processes.
We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference.
MA-SAC can be employed in both cooperative and competitive settings.
Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios.
While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.
The traditional reinforcement learning (RL) paradigm, that formalizes learning based on trial and error, has primarily been developed for scenarios where a single trainable agent is learning in an environment.
In this setup, although the agent changes its behavior as learning progresses, the environment dynamics themselves do not change.
Thus, the environment appears to be stationary from the point of view of the learning agent.
However, in a setting where several agents are learning in the same environment simultaneously (multi-agent setting), this is not true as a change in one agent's behavior manifests itself as a change in environment dynamics from the point of view of other agents (Busoniu et al., 2008) .
It has been established that stability issues can arise if each agent is independently trained using standard single-agent RL methods (Tan, 1993) .
While, in theory, it is possible to treat a collection of multiple agents as a single centralized metaagent to be trained, in practice, this approach becomes infeasible as the action space of the centralized meta-agent grows exponentially with the number of agents.
Moreover, executing the resultant centralized policy is not always possible due to various reasons like geographic separation between agents, communication overhead and so on (Foerster et al., 2018b) .
Even if these issues are taken care of, when the agents are competitive, designing a reward function for the centralized meta-agent is very challenging and thus, in general, such a setup cannot be used with competitive agents.
There are numerous practical scenarios that require several intelligent agents to function together (either cooperatively or competitively).
Consider, for instance, a soccer game between two teams: agents within a team must cooperate while being competitive with the opponents.
Considering that traditional single-agent RL methods cannot satisfactorily handle problems from the multi-agent domain, completely new RL algorithms that explicitly acknowledge and exploit the presence of other intelligent agents in the environment are required.
In this paper, we pose the multi-agent reinforcement learning problem as the problem of performing probabilistic inference in a particular graphical model.
While such a formulation is well known in the single-agent RL setting (Levine, 2018) , its extension to the multi-agent setup is non-trivial especially in the general case where the agents may be cooperative and/or competitive.
We model the environment as seen by each of the agents using separate but related Markov Decision Processes (MDPs).
Each agent then tries to maximize the expected return it gets from the environment under its own MDP (Section 4).
Using our framework, we derive an off-policy maximum entropy actor-critic algorithm that generalizes the Soft Actor-Critic (SAC) algorithm (Haarnoja et al., 2018a ) to a multi-agent setup.
We refer to this algorithm as Multi-agent Soft Actor-Critic (MA-SAC).
Like SAC, it is a maximum entropy algorithm, i.e., the learned policies try to maximize the rewards while at the same time maximizing entropy of the stochastic actor.
Such algorithms are known to be more stable and easier to train (Haarnoja et al., 2018a) .
MA-SAC follows the centralized training, decentralized execution paradigm.
As we demonstrate in Section 4, each agent learns its own policy while being actively aware of the presence of other agents.
The learned policy of any given agent only utilizes its local observation at test time.
Thus, MA-SAC avoids the pitfalls of both independent training of agents (being unaware of other agents leads to non-stationarity and hence instability) and training a centralized agent (centralized policies are hard to execute) as described above.
By setting a tunable temperature parameter (Section 4.3) to zero, MA-SAC yields an algorithm that is very similar to the Multi-agent Deep Deterministic Policy Gradients algorithm (MADDPG) (Lowe et al., 2017) apart from a minor change in updating the actor.
The utility of this modification is clearly reflected in our derivation of the inference procedure.
When the temperature parameter is non-zero, agents trained using MA-SAC outperform agents trained using MADDPG on multiple cooperative and competitive tasks as we demonstrate in Section 5.3.
Our main contributions are:
(i) we present a probabilistic view of the multi-agent reinforcement learning problem where each agent models the environment using a separate but related MDP;
(ii) we derive an off-policy maximum entropy actor-critic algorithm (MA-SAC) by performing structured variational inference in the proposed model;
(iii) we empirically demonstrate that MA-SAC performs well in practice and highlight different ways in which our framework can utilize ideas from other existing approaches in multi-agent RL; and
(iv) although we only present an actor-critic algorithm in this paper, our framework allows derivation of maximum-entropy variants of other reinforcement learning algorithms in the multi-agent setting.
In this paper we posed the multi-agent RL problem as the problem of performing probabilistic inference in a graphical model where each agent views the environment as a separate MDP.
We derived an off policy maximum entropy actor-critic algorithm based on the centralized training, decentralized execution paradigm using our proposed model.
Our experimental results show that the proposed algorithm outperforms a strong baseline (MADDPG) on several cooperative and competitive tasks.
As noted in Section 5.4, various existing ideas for parameterizing Q-functions (Yang et al., 2018; Rashid et al., 2018; Iqbal & Sha, 2019) can be naturally integrated with MA-SAC to improve its scalability as the number of agents increases.
Our framework can also be used for deriving maximum-entropy variants of other RL algorithms in the multi-agent setting.
We leave these ideas for future work.
A probabilistic framework for multi-agent reinforcement learning