2018 Explore Learning Answers

[Brayan Sedillo]

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I am implementing Q-learning algorithm and I observed that my Q-values are not converging to optimal Q-values even though the policy seems to be converging. I defined the action selection strategy as epsilon-greedy and epsilon is decreasing by 1/N starting from 1(N being the total number of iterations). That way in the earlier iterations the algorithm explores random states then this rate gradually decreases leading to exploitation. In addition, I defined the learning rate as 1/N_t(s,a) where N_t(s,a) is the total number of times (s,a) is visited.

Everything seems to be correct but since I can't get to the optimal Q-values I started looking into different strategies and in the meantime got super confused. I know that convergence is achieved when all (s,a) pairs are visited infinitely often. Isn't this equivalent to saying all (s,a) pairs are explored many times? In other words, why do we need exploitation for convergence? What if we don't exploit and just focus on exploring? If we do that we search all of the solution space, hence shouldn't that be enough to find an optimal policy?

Probably there is a simple answer to all of these however even though I checked a lot of resources and similar threads I still couldn't figure out the logic behind exploitation. Thanks a lot for your time in advance!

Sometimes we are not learning just for the sake of learning, but we also care about our performance already during the learning/training process. This means we need a balance between exploitation (performing well) and exploration (continuing to learn).

More importantly, if we purely explore and do not exploit at all, this may also limit our ability to learn in practice, because there are many states that we may simply fail to reach if we always act randomly.

To clarify on the second point, consider, for example, that we're in one corner of a large, 2D grid, and our goal position is in the opposite corner. Suppose that we already get small rewards whenever we move closer to the goal, and small negative rewards whenever we move further away. If we have a balance between exploration and exploitation, it is likely that we'll quickly learn to walk along the path from start to goal, but also bounce around that path a bit randomly due to exploration. In other words, we'll start learning what to do in all states around that path.

Now, suppose you try learning in the same situation only by acting randomly (e.g. no exploitation). If we only act randomly in a sufficiently large 2D grid, and we always start in one corner, it's highly unlikely that we'll ever manage to reach the other side of the grid. We'll just randomly keep moving in an area around the starting position, and never learn what to do in states far away from this starting position. It's unlikely to ever reach them with pure random behaviour in practice. Obviously we will reach every state given an infinite amount of time, but we rarely have an infinite amount of time in practice.

As you already said, from a theoretical point of view, RL methods always requires that all (s,a) pairs are visited infinitely often. However, exploitation stage is only necessary depending on the type of RL algorithm. A key concept relevant to your question is distinguishing between on-policy and off-policy algorithms.

In on-policy algorithms (e.g. SARSA) the agent should interact with the environment using the same policy it is being learned. So, this kind of methods requires using the learned policy (aka exploitation) in order to achieve convergence.

Off-policy methods can be very useful in problems where the data of interactions between agent-environment is collected in advance. For example, in a medical problem where you have stored interactions between physician treatment-patient responses, you could apply an off-policy algorithm to learn the optimal treatment. In this case obviously you are not using exploitation because the agent is not interacting the environment after the learning starts.

However, notice that off-policy methods can be also employed using explotation, although it's should be clear that this is not a requirement. In most typical RL problems, the goal is the agent chooses right actions as soon as possible. In such a case, make sense to start balancing between exploration-explotation just after learning starts, independenlty if the algorithm is on-policy or off-policy.

Inquiry-based learning is important for creating excitement in students. It motivates students to become specialists of their learning process. However, this type of learning requires a certain level of independent learning skills. Children need to have developed the information-processing skills needed for working with minimal guidance. In this article, we will argue that there is a place for this type of learning but it does need to be supported with appropriate teacher training and balanced with more traditional curriculum delivery.

Inquiry-based learning puts the student at the center of the learning process. Instead of simply absorbing information, students are encouraged to explore and discover knowledge on their own. This approach allows students to develop critical thinking and problem-solving skills, as well as a deeper understanding of the subject matter. The learning process becomes more engaging and meaningful, as students take ownership of their education and develop a sense of curiosity and wonder. However, it's important to remember that inquiry-based learning is just one approach to education and should be balanced with other teaching methods to ensure a well-rounded education.

The inquiry-based structure of learning has a lot of flexibility. Teachers frequently begin from inquiry-based science lessons, but the inquiry-based approach can be implemented into student learning to any lesson and subject. These transferable skills can be used to help pupils become more effective learners in the long run. In higher education, students are required to manage their own time and do their own research. This approach to teaching is a way of building skills for the long term.

Inquiry-based teaching strategies also support Science teacher while encouraging students to think deeper in Science lessons. Learners may brainstorm questions of their interest and discuss topics that amuse them.

Incorporating inquiry-based learning into your classroom might seem daunting, yet it holds immense potential for fostering deep learning and enhancing conceptual understanding. Let's break it down into practical steps that can be seamlessly integrated into your classroom practice.

Inquiry-Based Teaching Methods provide an exciting way to learn and teach. However, teacher professional development and training are important, not only for inquiry-based learning but also for student success. To create engaged and meaningful learning experiences in a classroom, schools must provide teacher training opportunities to teachers to teach these inquiry-based lessons successfully.

Schools need to build time into the curriculum for these types of autonomous exercises as they are essential life skills. However, not all subject material is appropriate for this sort of approach to education. Pupils will need to have the learning skills and cognitive attributes to run with these methods.

There will always be a body of knowledge that just needs to be taught from the front, picking the topics suitable for this type of approach is half the battle. If a student is not well practised or confident in the area of independent learning then they may develop knowledge gaps that hinder their learning.

These studies collectively emphasize the positive impact of inquiry-based learning on various student outcomes, including motivation, self-efficacy, academic performance, and the development of critical thinking and problem-solving skills.

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