Description: A non-stationary environment in the context of reinforcement learning refers to a system where statistical properties change over time. This means that decisions and strategies that may have been effective in the past can become ineffective as the environment evolves. This type of environment poses a significant challenge for learning algorithms, as they need to continuously adapt to new conditions and patterns. The main characteristics of a non-stationary environment include variability in rewards, changing dynamics of states, and the need for constant exploration to discover new strategies. The relevance of studying these environments lies in their applicability to real-world situations, where conditions can be unpredictable and subject to rapid changes, such as in various technological systems, market dynamics, or user interactions across digital platforms. In summary, a non-stationary environment demands a more robust and flexible approach in reinforcement learning, where adaptability and responsiveness are crucial for the success of the learning agent.
