Reinforcement learning (RL) is a popular approach to training autonomous agents that can learn to perform complex tasks by interacting with their environment. RL enables them to learn the best action in different conditions and adapt to their environment using a reward system.
A major challenge in RL is how to explore the vast state space of many real-world problems efficiently. This challenge arises due to the fact that in RL, agents learn by interacting with their environment via exploration. Think of an agent that tries to play Minecraft. If you heard about it before, you know how complicated Minecraft crafting tree looks. You have hundreds of craftable objects, and you might need to craft one to craft another, etc. So, it is a really complex environment.
As the environment can have a large number of possible states and actions, it can become difficult for the agent to find the optimal policy through random exploration alone. The agent must balance between exploiting the current best policy and exploring new parts of the state space to find a better policy potentially. Finding efficient exploration methods that can balance exploration and exploitation is an active area of research in RL.
It’s known that practical decision-making systems need to use prior knowledge about a task efficiently. By having…
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