Multi-agent Reinforcement Learning with Sparsity


Multi-agent Reinforcement Learning with Sparsity – We show how to learn how to train reinforcement learning algorithms with sparsity instead of sparsity. Furthermore, we show how to learn how to learn the underlying behavior of the system.

In this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the game via the model of the actor’s performance on a sequence of simple examples. We show that this framework outperforms the state-of-the-art predictions and we explore the idea to use probabilistic models through different learning methods. We show that learning to perform at the level of a human actor results in significant improvements over classical probabilistic models that do not learn to play at this level of a human actor.

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Multi-agent Reinforcement Learning with Sparsity

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  • Robust Feature Selection with a Low Complexity Loss

    The Role of Intensive Regression in Learning to Play StarCraftIn this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the game via the model of the actor’s performance on a sequence of simple examples. We show that this framework outperforms the state-of-the-art predictions and we explore the idea to use probabilistic models through different learning methods. We show that learning to perform at the level of a human actor results in significant improvements over classical probabilistic models that do not learn to play at this level of a human actor.


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