2019 Explore Learning Answer Key

[Elwanda Menhennett]

Find out how online interactive simulations can help enhance your STEM curriculum, support student math achievement, effect growth mindset and more in our webinar series. Gizmos and Reflex enhance students' success in math and science, and ExploreLearning provides professional development to teachers to build their own success in the classroom.

High-quality, early-learning environments provide children with a structure in which to build upon their natural inclination to explore, to build, and to question. Research confirms that early STEM skills are a great predictor of later academic success overall.

Join us for this complimentary webinar to discover how Science4Us, our K-2-focused program, can help you teach and promote STEM early on, while integrating English literacy through short stories, hands-on and digitally interactive activities, and much more!

The most effective classroom instructional strategies use inquiry-based methods that connect course content with real-world applications to demonstrate the relevance of the curriculum and support academic success.

Please join us for this complimentary webinar to discuss how Gizmos STEM Cases allow students to apply course content in a meaningful context. These interactive, real-life experiences deepen students' understanding of what they are learning, strengthen their problem-solving and critical thinking skills, and make the subject matter more engaging and easier to master for all learners.

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Our award-winning science curriculum immerses students in real-world phenomena through engaging lessons, interactive features, and high quality media, such as BBC Streaming. Tailored to your state standards, our curriculum brings science to life.

Turn screen time into learning time with STEMscopes Coding (powered by Bitbox)! STEMscopes Coding shows students how to build, customize, and share their own digital apps with typed JavaScript code. Students get a taste of life as a professional coder.

Students love tinkering with toys, taking them apart and putting them back together. Dive-in Engineering fosters that curiosity by encouraging students to deconstruct, imitate, vary, explore real-world engineering problems, and then design their own solutions.

Built on the 5E lesson model, STEMscopes Math shows students how math works in our everyday experiences. STEMscopes Math also provides the resources teachers need to promote meaningful learning that empowers their students with 21st-century skills for real-world application.

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.

In the year since the pandemic brought the country to a halt, state policymakers faced challenges transitioning to a remote work and schooling environment and began to lay the groundwork for long-term recovery. At each step along the way, Education Commission of the States has served the people behind the policy by ensuring states have the information they need to make informed decisions. Information requests are one way we provide research and counsel to our constituents as they address pressing issues in their states.

Since the shutdown, we have responded to over 560 requests for information from state policymakers, media outlets and other education stakeholders, with nearly 130 of them in 2021 alone. We frequently review these requests to identify similar challenges states are facing across the country. In February, state policymakers had questions about key issues related to student learning loss and equitable student transitions because of the uncertainty caused by the pandemic.

Preliminary learning loss data coupled with other state efforts to assess student progress will provide states with a more complete picture of student performance to better target resources and interventions. With this data in hand, some states are asking for information about the best ways to help students get caught up, and fast. In responses prepared for legislative staff, state education agency personnel and state board of education members, ECS staff highlighted research that emphasized intensive tutoring and increased instructional time as important levers to accelerate student learning.

ECS policy staff also identified promising state legislative examples in Tennessee and Minnesota. Tennessee House Bill 7004 and Senate Bill 7002 require the department of education to establish and administer a learning loss remediation program to support after-school learning mini-camps, learning loss bridge camps and summer learning camps. Minnesota Senate File 64 would create math and reading corps to provide services to students whose learning has been impacted by the pandemic.

In addition to learning loss, students have had difficulties satisfying graduation and college admissions requirements, leading many states, governing boards and institutions to implement graduation requirement waivers and test-optional admissions policies. Following the immediate response to the conditions of the pandemic, states have shifted their focus to long-term changes to student transition policies.

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