Developing Smart Sample-efficient exploration strategies for Reinforcement learning agents with a focus on its application for autonomous robot contro

探索是强化学习的主要挑战之一，它指的是主体所经历的各种状态和动作。有效的探索程序是当代理人积极寻求可能导致更高长期回报的新状态和行动时。在现实世界的许多RL情况下，我们处理的是多维和连续的动作空间，其中不能期望代理访问每个状态，因此需要有效的探索策略。机械臂是用于测试连续动作空间的强化学习算法的真实世界的常见用例。这个项目的主要目的是发展和改进现有的强化学习探索策略的方法。这方面的主要测试案例将是使机械臂自动化，以有效地执行RL任务。潜在的好处和直接应用包括开发自动驾驶汽车的路线选择算法，提高能效供暖和空调系统，使用自主机器人医疗仪器进行更安全的手术，机器翻译等。

Exploration is one of the major challenges in reinforcement learning (RL) and it refers to the variety of states and actions that the agent experiences. An effective exploration procedure is when an agent actively seeks novel states and actions that may lead to higher long term rewards. In many RL cases in the real world we deal with multi-dimensional and continuous actions spaces in which the agent cannot be expected to visit every state and therefore effective exploration strategies are needed. Robotic arms are a real world common use case for testing reinforcement learning algorithms for continuous action spaces. The main aim of this project is to develop and improve upon the existing state of the art methods in exploration strategies for reinforcement learning. The main test case for this will be to automate a robotic arm to execute RL tasks efficiently. Potential benefits and direct applications include developing route taking algorithms for self-driving cars, improving energy efficiency heating and air-conditioning systems, safer surgery with autonomous robotic medical instruments, machine translation, etc.