In reinforcement learning, which strategy involves the agent selecting actions at random to explore new possibilities?

In reinforcement learning, which strategy involves the agent selecting actions at random to explore new possibilities?

• A. Exploration (Random) strategy
• B. Epsilon Greedy Strategy
• C. Decay Factor
• D. Multi-Arm Bandit Problem

Answer: (A)

In reinforcement learning, exploring the action space by selecting actions at random is about discovering unknown rewards rather than sticking to what is already known. This approach, called the Exploration (Random) strategy, explicitly samples actions uniformly at random to probe new possibilities and gather information about their potential payoffs. It embodies pure exploration without bias toward current estimates, which is why it fits the question. In contrast, the epsilon-greedy strategy mainly exploits the best-known action but occasionally picks a random action to explore; the random choices there are limited by a fixed probability and are not the primary mode of operation. The decay factor typically refers to decreasing parameters like a learning rate or a discount factor over time, not a policy that governs random action selection. The Multi-Arm Bandit Problem is a framework for studying exploration vs. exploitation, not a standalone policy of random action selection. So the option that describes selecting actions at random to explore new possibilities is the Exploration (Random) strategy.

In reinforcement learning, exploring the action space by selecting actions at random is about discovering unknown rewards rather than sticking to what is already known. This approach, called the Exploration (Random) strategy, explicitly samples actions uniformly at random to probe new possibilities and gather information about their potential payoffs. It embodies pure exploration without bias toward current estimates, which is why it fits the question.

In contrast, the epsilon-greedy strategy mainly exploits the best-known action but occasionally picks a random action to explore; the random choices there are limited by a fixed probability and are not the primary mode of operation. The decay factor typically refers to decreasing parameters like a learning rate or a discount factor over time, not a policy that governs random action selection. The Multi-Arm Bandit Problem is a framework for studying exploration vs. exploitation, not a standalone policy of random action selection.

So the option that describes selecting actions at random to explore new possibilities is the Exploration (Random) strategy.