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 do replay buffers and target networks contribute to the stability and efficiency of deep Q-learning algorithms?
Deep Q-learning algorithms, a category of reinforcement learning techniques, leverage neural networks to approximate the Q-value function, which predicts the expected future rewards for taking a given action in a particular state. Two critical components that have significantly advanced the stability and efficiency of these algorithms are replay buffers and target networks. These components mitigate
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Deep reinforcement learning, Function approximation and deep reinforcement learning, Examination review