What is Deep Reinforcement Learning?
Deep Reinforcement Learning (DRL) is an advanced machine learning technique that combines reinforcement learning (RL) with deep learning (DL) methods. It empowers agents to make optimal decisions by learning from their interactions in complex environments and receiving feedback in the form of rewards or penalties.
In DRL, deep neural networks serve as function approximators to process high-dimensional input data, such as images and raw sensor data. These networks enable the agent to discern patterns and representations crucial for decision-making. The agent learns through trial and error, exploring various strategies while refining its approach based on the rewards received from the environment.
There are two main components in DRL: the policy and the value function. The policy dictates the agent's action selection in given states, while the value function estimates the expected future rewards from those states. By balancing exploration (trying new actions) and exploitation (utilizing known effective actions), DRL enables agents to develop sophisticated strategies for achieving goals.
DRL has gained popularity due to its successful applications in various domains, including robotics, game playing, and autonomous systems. Technologies such as AlphaGo, which defeated world-class human players, exemplify the potential of deep reinforcement learning, showcasing its capacity to tackle complex challenges that require strategic thinking and adaptability.