Improving deep reinforcement learning with advanced exploration and transfer learning techniques - Yin, Haiyan (Nanyang Technological University, 2020)
Deep reinforcement learning utilizes deep neural networks as the function approximator to model the reinforcement learning policy and enables the policy to be trained in an end-to-end manner. When applied to complex real world problems such as video games playing and natural language processing, the deep reinforcement learning algorithms often engage tremendous parameters with intractable search space, which is a result from the low-level modelling of state space or the complex nature of the problem. Therefore, constructing an effective exploration strategy to search through the solution space is crucial for deriving a policy that can tackle challenging problems. Furthermore, considering the considerable amount of computational resource and time consumed for policy training, it is also crucial to develop the transferability of the algorithm to create versatile and generalizable policy.