Week 9. May. 17: Reinforcement Learning - Orienting

[JunsolKim]

Post your questions here about: “Reinforcement Learning” and “Deep Reinforcement Learning”, Thinking with Deep Learning, Chapters 15 & 16

[kceeyang]

In chapter 18, “Reinforcement Learning,” the author briefly mentioned the difference between the off-policy algorithm, such as the Q-Learning algorithm, and the on-policy algorithm, such as the Policy Gradients algorithm. How do we decide which algorithm to use and when to utilize them? And since on-policy algorithms tend to learn quicker, would this make them more efficient than off-policy algorithms?

[guanhongliu2000]

I got a question about the Chapter 18, "Reinforcement Learning," the author touches upon the distinction between off-policy algorithms, like Q-Learning, and on-policy algorithms, such as Policy Gradients. Choosing between these types of algorithms depends on the specific requirements and constraints of the problem at hand. Off-policy algorithms learn from actions that are outside the current policy, allowing them to leverage past experiences more effectively. This can be particularly useful in environments where data collection is expensive or where the exploration of new strategies is risky. On the other hand, on-policy algorithms learn directly from the actions taken by the current policy, which can lead to faster learning as they adapt more quickly to the policy's performance. While on-policy algorithms often learn more rapidly, making them seem more efficient, their effectiveness depends on the problem's context and the trade-offs between exploration and exploitation. Ultimately, the choice of algorithm should be guided by the specific goals and constraints of the task, such as the need for stability, speed of learning, and the ability to handle changing environments.

[uc-diamon]

In regards to Reinforcement Learning, how does the algorithm handle doing this "trial and error" on high-dimensional data as well as handle a target that is far into the future or requires many actions before the final outcome?

[HongzhangXie]

In Deep Reinforcement Learning, it is essential to abstract problems into a Markov Decision Process (MDP). A defining characteristic of the Markov process is that the state of the system at the next instant is determined solely by its current state, independent of previous states. However, some factors may have a long-term influence on outcomes in real-world applications. For example, in targeting users on a social platform with potentially interesting content, their earlier browsing history could also be informative. To address this challenge, various methods such as Recurrent Neural Networks, attention mechanisms, State Augmentation, Model Predictive Control (MPC), and external memory mechanisms can be employed. What are the advantages and typical application scenarios of each of these methods?

[CYL24]

Based on the readings, it seems that in the context of large state/action spaces or high dimensional space, Double Q-learning often exhibits greater stability compared to both traditional Q-learning and Delayed Q-learning. Then how do different function approximation methods, such as neural networks, decision trees, or linear models, impact the performance and scalability of Double Q-learning in high-dimensional state spaces?

[erikaz1]

Drawing from both the Winder and Geron readings: Will RL algorithms applied to the same task converge to the same optimal solution or optimal learning processes, despite randomly selecting actions and being encouraged to explore? I would expect the affirmative in most cases because most of these models work in a constrained environment and are not all-purpose (or purposeless), and therefore have constrained inputs/outputs that result in a defined hierarchy of expectations.

[Marugannwg]

Unlike what we attempted to use deep learning to discover hidden patterns, reinforcement learning seems to (always?) optimize some objective through a trial and error manner. I wonder how what if the local objective and rewards is misleading or getting exploited by the algorithm? Geron's tutorial mentioned applying the algorithm to robotics in a real-world setting -- I can envision it to be a much more complicated reward system. (e.g. how'd you reward a robot to learn to kick?)

[Pei0504]

Chapter 18 of "Reinforcement Learning" contrasts off-policy algorithms and on-policy algorithms Off-policy algorithms use past actions, making them suitable for costly or risky data collection scenarios. What are the benefits and common uses of RNNs, attention mechanisms, State Augmentation, MPC, and external memory mechanisms for managing long-term dependencies in Deep Reinforcement Learning?

[maddiehealy]

In regards to orienting our understanding of reinforcement learning, I am interested in RL as a discipline, specifically its beginnings. When browsing for possibility readings this week, I came across a lot of suggested psychology reading. Winder’s paper talks briefly about RL’s relation to psychology, but I am curious to learn more. To what extent is RL based on psychological principles?

[anzhichen1999]

Considering the distinctions between off-policy and on-policy algorithms in reinforcement learning, and the challenges of handling high-dimensional data and long-term dependencies, how should one approach the selection and implementation of these algorithms for specific applications?

[XueweiLi1027]

In reinforcement learning, agents learn from interactions with their environment to maximize cumulative rewards. However, balancing exploration of new actions versus exploitation of known strategies to gain rewards presents a challenge. I therefore was wondering about some commonly used strategies that could help address the exploration-exploitation trade-off. Additionally, how do techniques like ε-greedy, softmax selection, and Upper Confidence Bound (UCB) contribute to effectively navigating this trade-off in different reinforcement learning environments?

[kangyic]

It seems that we barely have application of RL in social science, is it possible to apply it say in behavirol economics? To study behaviors logit behind purchasing.

[00ikaros]

What are the key distinctions between off-policy algorithms like Q-Learning and on-policy algorithms such as Policy Gradients in reinforcement learning, and how do these differences influence their effectiveness in various contexts? Specifically, how do off-policy algorithms leverage past experiences and why is this beneficial in environments with expensive data collection or risky exploration? Conversely, how do on-policy algorithms adapt more quickly to the current policy's performance, and what are the trade-offs between exploration and exploitation in this context? Lastly, how should one decide between using off-policy and on-policy algorithms based on the specific goals and constraints of a task, such as stability, speed of learning, and handling changing environments?

[HamsterradYC]

How can reinforcement learning systems be designed to ensure that the reward optimization does not lead to ethical violations or safety concerns, particularly in critical applications like healthcare and autonomous driving?

[Carolineyx]

How do the convergence properties of different reinforcement learning algorithms, such as Q-learning and policy gradient methods, compare in terms of their theoretical guarantees and practical performance? What are the current challenges in ensuring convergence and stability in these algorithms?

[Xtzj2333]

What's the difference between multi-agent reinforcement learning and LLM agent based models?

[beilrz]

When should reinforcement learning (RL) be utilized, and is its application mostly limited to online settings? Additionally, under what conditions should a model be designed to explore novel data? for offline setting, how can we ensure the strategy we found is applicable to the online environment also, since we do not have the ability to explore novel data in offline environment.

[mingxuan-he]

For deep reinforcement learning models powering physical devices, how does the reinforcement learning algorithm resolve the differences between noisy real-world environments and the simulated training environment?

[hantaoxiao]

1. Exploration vs. Exploitation: How can reinforcement learning algorithms be designed to dynamically balance the trade-off between exploration and exploitation, especially in environments where exploration is costly or risky?
2. Ethical Considerations: In what ways can reinforcement learning systems be implemented to ensure ethical decision-making and avoid harmful consequences, particularly in sensitive applications like healthcare or autonomous driving?

[Brian-W00]

In practical applications, how to solve the adaptability problem of reinforcement learning models in the face of real-world complexity and uncertainty? Furthermore, what are the main technical challenges and limitations that these models typically encounter when applied across domains?

[MarkValadez]

I found in my assignment work that policy gradient appeared to converge more quickly at an optimal solution. I wonder how it compares to Q-Learning in the cases were we add some Markov transition to allow for some random jumps so that it does not only drift toward the local gradient but is able to explore other paths. From, this same perspective, I wonder if the policy gradient operates better in continuous domains by virtue of its construction while q learning benefits from a discrete set of possible actions to optimize the q-values over. Is there any case where the inverse would be better for either model?

[Pei0504]

How can Deep Reinforcement Learning techniques overcome the limitations of the Markov Decision Process (MDP) assumption, which states that future states depend only on the current state and not on past states? Methods such as Recurrent Neural Networks (RNNs), attention mechanisms, State Augmentation, Model Predictive Control (MPC), and external memory systems have been proposed to address long-term dependencies. What are the distinct advantages of each method, and in what application scenarios, such as user engagement on social media, are these methods most effectively applied?