Essays about: "deep multi-agent reinforcement learning"
Showing result 6 - 10 of 21 essays containing the words deep multi-agent reinforcement learning.
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6. Reinforcement Learning for Pickup and Delivery Systems
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this project multi-agent reinforcement learning (RL) for a warehouse environmentwith robots delivering packages has been studied. This was done by first implementing the RLalgorithm Q-learning and investigating how the parameters of Q-learning affect the performanceof the algorithm. READ MORE
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7. Multi-Agent Control in Warehousing: A Deep Q-Network Approach
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With an increase in consumption, warehouses increase in size and demand for fastdistribution of goods. One solution to this problem is self learning robots that can adapt toany warehouse. READ MORE
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8. Scalable Deep Reinforcement Learning for a Multi-Agent Warehouse System
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This report presents an application of reinforcementlearning to the problem of controlling multiple robots performingthe task of moving boxes in a warehouse environment. The robotsmake autonomous decisions individually and avoid colliding witheach other and the walls of the warehouse. READ MORE
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9. Explainable Reinforcement Learning for Remote Electrical Tilt Optimization
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Controlling antennas’ vertical tilt through Remote Electrical Tilt (RET) is an effective method to optimize network performance. Reinforcement Learning (RL) algorithms such as Deep Reinforcement Learning (DRL) have been shown to be successful for RET optimization. READ MORE
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10. Modelling Financial Markets via Multi-Agent Reinforcement Learning : How nothing interesting happened when I made AI trade with AI
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The numerous previous attempts to simulate financial markets tended to be based on strong assumptions about markets or their participants. This thesis describes a more general kind of model - one in which deep reinforcement learning is used to train agents to make a profit while trading with each other on a virtual exchange. READ MORE
