LESSON 15 - Reinforcement learning Flashcards
What is the significance of manipulating the sensory environment in building causal models, and how does it relate to reinforcement learning?
Manipulating the sensory environment is crucial for building causal models in reinforcement learning. Causal models involve understanding the relationships between actions and consequences, making it essential to go beyond mere correlations.
How does the example of eating chocolate and winning a Nobel Prize illustrate the concept of spurious correlation?
The example illustrates spurious correlation, as there is a strong correlation between eating chocolate and winning a Nobel Prize, but it is not a causal relationship. It emphasizes the importance of discerning genuine causation from mere statistical association.
In the context of smoking and cancer, what approach did Morris and Fisher take to defend against the claim that smoking causes cancer?
Morris and Fisher defended against the claim that smoking causes cancer by considering alternative explanations. They explored the possibility that cancer might cause a desire to smoke or that a hidden gene caused both cancer and smoking.
Briefly explain the concept of counterfactual reasoning and its utility as a statistical tool.
Counterfactual reasoning is a powerful statistical tool that involves considering what might have happened under different circumstances. It helps in assessing causal relationships by comparing observed outcomes with hypothetical scenarios, even though detailed specifics are not necessary.
How does exploration differ from exploitation in the context of reinforcement learning?
In reinforcement learning, exploitation involves continuing actions that are already known to yield rewards, while exploration entails trying new actions to discover potentially better outcomes. Striking a balance between exploration and exploitation is crucial for effective learning.
Define the finite-horizon case in reinforcement learning, and explain the role of the discount rate.
In the finite-horizon case, reinforcement learning involves deciding when to stop actions, denoted by capital T. The discount rate, denoted as G, is a value between 0 and 1 that determines how much importance should be given to future rewards. It introduces the notion of caring about future rewards, addressing cases where T might be infinite.
What is the value function in reinforcement learning, and how is it computed?
The value function assigns a number to each state, indicating its goodness. It is computed as the expected return under a certain policy. The value of a state can be decomposed into the sum of the current reward and the expected future rewards, which is refined through consistent observations over time.
Explain the concept of a policy in the context of reinforcement learning.
A policy in reinforcement learning defines the probability of taking a certain action given a specific state. It guides an agent’s decision-making by providing a strategy for selecting actions. Understanding both state values and associated policies helps plan future behavior and optimize rewards.
What is the Bellman equation in reinforcement learning, and how does it relate to the value of a state under a certain policy?
The Bellman equation expresses the value of a state under a certain policy as the expected return. It captures the relationship between the current reward, the value of the next state, and the discount rate. It provides a foundation for developing algorithms to maximize cumulative rewards.
What is TD learning, and how is it applied in reinforcement learning algorithms?
Temporal Difference (TD) learning is a reinforcement learning algorithm. In the simplest form, it updates the current state based on a prediction error, which is the difference between a better estimate and the current estimate. TD learning is fundamental in refining value function estimates.
How does the application of deep learning enhance reinforcement learning, especially in the context of deep reinforcement learning?
The application of deep learning to reinforcement learning transforms the agent into a neural network, directly outputting a policy based on sensory inputs. This approach, known as deep reinforcement learning, makes the learning process more efficient by assigning probabilities to different actions and selecting the action with the highest probability.
In 2015, what notable achievement in reinforcement learning demonstrated the ability of AI to play Atari games?
In 2015, reinforcement learning systems were trained to play Atari games by deciding actions based on pixel information. This demonstrated that AI could learn to play games without explicit instructions, learning to make sense of the environment and achieving goals independently.
What recent accomplishment showcases the capabilities of AI in beating a Go champion?
A recent accomplishment in AI showcases its ability to beat a Go champion, demonstrating the high-level strategic and decision-making capabilities of artificial intelligence.
In the context of Atari games, what distinguishes more complex games like Atari Revenge, and why is common sense knowledge required?
More complex games like Atari Revenge require common sense knowledge, unlike simpler games. Common sense knowledge is necessary for understanding the goals and rules of the game, making the learning process more challenging.
In multi-agent reinforcement setups, what observed behavior led to the discovery of emergent communication among agents?
In multi-agent reinforcement setups, agents slowly learned to collaborate, leading to the discovery of emergent communication. Agents developed a form of communication to enhance collaboration, resulting in the emergence of machine language.
