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About Multi-Agent Deep Reinforcement Learning: Maxim Egorov Stanford University
This document introduces a novel approach for applying deep reinforcement learning techniques to solve multi-agent reinforcement learning problems. The approach reformulates the multi-agent problem state into an image-like representation that can be input into a convolutional neural network to learn distributed multi-agent policies. The approach is shown to generalize policies across different environment sizes and numbers of agents. Transfer learning is also used to decrease the time needed to learn policies for large groups of agents.
Detailed Information
| Author: | ['Creativ Pinoy'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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