Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent modeling: the first one based on parameter sharing, and the second one based on agent policy features... (read more)
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Entropy Regularization
Dense Connections
Softmax
Convolution
A3C