Active Reinforcement Learning Vs. Passive Reinforcement Learning

Active learning in machine learning means that the model that is trained plays an active role in the process of acquiring knowledge. In other words, it gains knowledge on its own. There is another type of learning called passive, where the central role is given to the educator, and the machines (learners) are only encouraged to internalize the material that has already been processed for them.

Reinforcement learning can also be active or passive. Further on, we will examine in more detail what active reinforcement learning in artificial intelligence is and how it is used, as well as its differences from passive RL.

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What is active reinforcement learning?

Active learning

First of all, let's understand what active learning is. As was already mentioned that in active learning the learner extracts the knowledge, in the case of ML, this knowledge is represented by data labels. The AI model, already partially trained on a small amount of labeled data, turns to a human to obtain labels for data it is not confident about. Since the system learning process involves providing inputs and expected outputs (labels), it is considered a supervised type of learning.

There are three key ways of how active learning algorithms work:

• Membership query synthesis. The AI creates its own synthetic data examples to be labeled;

• Stream-based selective sampling. The AI looks at examples one by one as they come and decides if it's confident enough to label them or if it needs help from a human expert;

• Pool-Based Sampling. The agent has a big collection of examples to choose from, picks one according to an informative score, and asks humans to label it.

Active vs Passive learning

In active learning, the algorithm engages with a human or an oracle to select the most informative samples for labeling. The algorithm iteratively selects unlabeled data points, requests their labels from an oracle, and updates the model based on the newly labeled data.

Passive learning relies on pre-collected labeled data. The labeled data is typically collected in advance, and the learning algorithm passively consumes this data to train the model without selecting or acquiring additional examples for labeling. Unlike active learning, which involves the selection of informative examples for labeling, passive learning does not involve any interaction or intervention during the training process.

From this perspective, passive learning may appear to be a more straightforward method of training a model. Since it simply requires collecting a set of labeled data and feeding it into an AI model to train it. However, it requires a huge amount of high-quality labeled data to train the model, which is not always an easy and affordable task.

Addressing a reliable data partner can resolve this issue. Achieving high-quality training data for a model entails diversity, ethicality, lack of bias, and consistency, which are precisely what Toloka specializes in. With a team of experts in various domains such as coding, natural sciences, STEM, medicine, and more, Toloka ensures the provision of top-tier data for training your AI applications. Get in touch with Toloka’s team to access high-quality data tailored to your specific needs.

Active learning, on the other hand, can significantly reduce the amount of labeled data required to train a model by selecting the most informative examples for labeling. This way it can achieve comparable performance to passive learning with a fraction of the labeled data. At the same time, active learning can help minimize labeling costs by prioritizing the most informative examples for annotation, reducing the overall labeling burden.

Reinforcement Learning

Reinforcement Learning (RL) is an AI model training process where the system rewards the agent for every right step, thus motivating it to make the following decision right. It doesn't use labeled examples, but rather reinforcement signals that let the agent know if the chosen action is correct. In the process of reinforcement learning for AI model is guided by policy which can be fixed or not. This is what determines if reinforcement learning is active or passive.

Passive Reinforcement Learning

Passive reinforcement learning utilizes a fixed policy that gives it a predefined set of actions that it should execute. Passive RL agent follows a fixed policy or set of actions without exploring alternative strategies. This policy remains fixed throughout the learning process. The agent observes the state of the environment and receives feedback (rewards) based on its actions, but it does not seek to influence the environment or explore alternative actions.

If the dynamics of the agent's environment are predictable and consistent over time, passive RL may be appropriate for learning optimal behavior. Passive reinforcement learning is best for stable environments with the optimal policy not changing frequently and for tasks where the agent's actions can be predetermined or externally controlled.

Active Reinforcement Learning

In active reinforcement learning the policy is unknown, so the system has to figure it out on its own to learn how to successfully solve tasks. Active RL agent engages with an environment to learn an optimal policy that maximizes its cumulative reward. The system actively decides which actions to take based on its current state and its learned policy.

To gather more information and improve its learning process, the active learning algorithm not only learns from the environment's feedback like an agent in passive RL but also actively explores and selects actions depending on the present state of the environment.

Active RL enables the agent to adapt its behavior in response to changes in the environment. By actively selecting actions and learning from their outcomes, the agent can continuously update its policy and adjust its behavior to achieve better performance in dynamic environments.

Therefore, active learning in RL is more beneficial in dynamic environments, where the optimal policy may change over time. It enables the agent to adapt to changes in the environment by continuously exploring and updating its policy based on new experiences.

Benefits of Active Reinforcement Learning

Adaptability to Dynamic Environments. Active RL enables agents to adapt to changes in the environment because they can explore and update their policies during training. This adaptability is crucial for tasks where the environment is dynamic or uncertain, as the agent can quickly learn and adjust its behavior to changing conditions;

Reliability. Active reinforcement learning in AI can produce more credible policies thanks to an agent's proactive search for a wide range of experiences during training. Doing so can help an agent gain a better understanding of unfamiliar situations and improve its performance in complex or unfavorable environments;

Complex problem solving. Active RL empowers agents to solve complicated problems through active exploration and use of the basic structure of the environment. What this ability does is allow agents to navigate through complex solution spaces and find optimal solutions to the challenges.

Conclusion

Active and passive learning are supervised learning paradigms, the difference between them is how they handle labeled data. The former specifies labels from a human or oracle, while the latter initially possesses a ready-to-use set of labeled data.

Reinforcement learning typically does not rely on labeled data in the traditional sense. Instead, RL algorithms learn through interaction with an environment, where they receive feedback in the form of rewards based on their actions. This feedback guides the agent to learn an optimal policy for decision-making in the environment.

There is a distinction between passive RL and active RL in terms of how the agent interacts with the environment but both strategies aim to train agents to make optimal decisions in an environment. Passive RL relies on predefined policies to learn from the environment's feedback. Agent in passive RL executes a known set of actions not trying to explore new ones. While suitable for certain tasks with well-defined objectives and relatively stable environments, passive RL may struggle to adapt to changes or efficiently solve complex tasks.

Active RL introduces a more dynamic learning paradigm where the agent can select actions to explore and gather new information from the environment. That way, active RL agents can adapt more effectively to evolving conditions. Both active and passive RL techniques are applied in various real-world domains such as robotics, game playing, healthcare, and more.

Thus, the choice between active and passive RL will depend on the unique requirements of the training task and the desired results. Through leveraging the strengths of both active and passive RL, researchers and practitioners will be able to advance the field and open new opportunities for intelligent decision-making in complex environments.