A random policy is a simple decision-making strategy commonly referenced in reinforcement learning and artificial intelligence. When an agent follows a random policy, it selects its next action uniformly at random from the set of all possible actions, regardless of the state it is currently in or any information about previous outcomes. This means the agent does not use any learned knowledge, reward signals, or heuristics to guide its choices. Instead, it acts as if it is rolling a die for each decision, resulting in purely stochastic behavior.
Random policies are often used as baseline comparisons in reinforcement learning experiments. Since they do not incorporate any learning or optimization, their performance is typically at or just above chance level. By comparing the performance of more sophisticated policies—such as those that use value functions, policy gradients, or greedy approaches—against a random policy, researchers can assess whether an algorithm is actually learning to make better decisions. If a new algorithm only performs as well as a random policy, it may indicate there is a bug, inappropriate reward structure, or insufficient exploration in the learning setup.
Beyond benchmarking, random policies have some practical uses. They can help initialize exploration in environments where the agent has no prior knowledge. In early training stages, acting randomly allows the agent to sample a wide variety of states and outcomes, which can be essential for learning about the environment. Some advanced exploration strategies in reinforcement learning even blend random actions with more directed behaviors to avoid getting stuck in local optima or missing out on rare but important events.
However, the limitations of a random policy are clear. Because the agent ignores all information about its environment and its own experiences, it cannot improve its behavior over time. This lack of adaptation means random policies are rarely useful for solving real-world tasks on their own. Still, understanding and using random policies as a building block or baseline is fundamental for anyone studying or working with AI agents and reinforcement learning systems.
In summary, a random policy is an agent strategy where actions are chosen completely at random, with no regard for state, outcome, or prior learning. While not useful for task-solving by itself, it is a helpful tool for benchmarking, exploration, and understanding the fundamentals of agent behavior in AI.
