Student Posters Repository

Reinforcement Learning methods have great potential in solving sequential decision making tasks. They can learn adaptive optimal control policies in areas that traditionally employ heuristic methods. However, contemporary RL methods are sample inefficient and the real-life problems have a very large state space to explore. We are looking at control application areas that offer a large population of RL agents that can collective learn by sharing their experiences. We research on efficient means of exploration and accelerating the learning process through distributed computing. Energy Harvesting Wireless Sensor Networks for Internet of Things is one such interesting application scenario. The sensor nodes need to learn to optimize their performance with respect to a number of parameters such as energy consumption, Quality of Service (QoS), utility of the sensory data etc. Furthermore, their working environment is highly unpredictable and diverse. Our research leverages the population of these nodes for efficient e-greedy exploration and learn optimal energy management policies to ensure optimal performance for perpetual operation. Our results show a 50x increase in learning performance at one-third of the cost by using DiRL. Future works include methods to decentralize the learning process and exploit the intelligence at the nodes.