Towards Information-Seeking Agents

Understand

We develop a general problem setting for training and testing the ability of agents to gather information efficiently.

• Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals.
• We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks.
• We shape the behavior of these agents by combining extrinsic and intrinsic rewards.