The paper studies the problem of multi-agent coordination in continuous action spaces. It proposes a novel algorithm that uses a combination of imitation learning and reinforcement learning to learn a policy that coordinates the actions of multiple agents. The algorithm is evaluated on a simulated environment where agents must cooperate to navigate a maze. The results show that the proposed algorithm can learn to coordinate the actions of multiple agents to achieve a common goal.
Who was the leader of the Soviet Union at the time of the Battle of Stalingrad?
Which country won the Battle of Stalingrad?
What was the total number of casualties suffered by the Soviet Union during the Battle of Stalingrad?
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