What are the main challenges associated with training neural networks using reinforcement learning, and how do techniques like experience replay and target networks address these challenges?
Training neural networks using reinforcement learning (RL) presents several significant challenges, primarily due to the inherent complexity and instability of the learning process. These challenges arise from the dynamic nature of the environment, the need for effective exploration, the stability of learning, and the efficiency of data usage. Techniques such as experience replay and target
How does function approximation help in managing large or continuous state spaces in reinforcement learning, and what are some common methods used for function approximation?
Function approximation plays a crucial role in managing large or continuous state spaces in reinforcement learning (RL) by enabling the generalization of learned policies and value functions across similar states. In traditional tabular RL methods, the state and action spaces are discretized, and values are stored in tables. This approach becomes impractical when dealing with