What role does experience replay play in stabilizing the training process of deep reinforcement learning algorithms, and how does it contribute to improving sample efficiency?
Experience replay is a crucial technique in deep reinforcement learning (DRL) that addresses several fundamental challenges inherent in training DRL algorithms. The primary role of experience replay is to stabilize the training process, which is often volatile due to the sequential and correlated nature of the data encountered by the agent. Additionally, experience replay enhances
What are the advantages and potential inefficiencies of model-based reinforcement learning, particularly in environments with irrelevant details, such as Atari games?
Model-based reinforcement learning (MBRL) is a class of algorithms in the field of reinforcement learning (RL) that utilizes a model of the environment to make predictions about future states and rewards. This approach contrasts with model-free reinforcement learning, which learns policies and value functions directly from interactions with the environment without an explicit model. MBRL