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What is the biological name of Reinforcement Learning?

Reflex Learning

Conditioned Learning

Associative Learning

Operant Conditioning (correct)

What is the main problem of assigning reward in Reinforcement Learning?

Defining a reward function that maximizes short-term objectives with immediate effects

Defining a reward function that accurately reflects long-term objectives without unintended side effects (correct)

Defining a reward function that maximizes long-term objectives with delayed effects

Defining a reward function that accurately reflects short-term objectives without unintended benefits

What type of action space and environment are suited for value-based methods?

Mixed action spaces and environments with static rules

Discrete action spaces and environments with clear rules (correct)

Hybrid action spaces and environments with dynamic rules

Continuous action spaces and environments with unclear rules

What is the difference between model-free and model-based methods in Reinforcement Learning?

Model-free methods do not use a model of the environment, while model-based methods do Signup and view all the answers

What are the two basic Gym environments?

Mountain Car and Cartpole Signup and view all the answers

What are the five elements of a Markov Decision Process (MDP)?

States, Actions, Transition probabilities, Rewards, Discount factor Signup and view all the answers

Which of the following is an application of Reinforcement Learning?

Sequential decision-making problems Signup and view all the answers

What is the purpose of the discount factor in Reinforcement Learning?

To balance the importance of short-term and long-term rewards Signup and view all the answers

What does Q(s, a) represent in reinforcement learning?

The expected cumulative reward starting from state s Signup and view all the answers

What is the principle used in dynamic programming?

Principle of Optimality Signup and view all the answers

What is the main idea behind recursion?

Dividing problems into simpler subproblems and solving them recursively Signup and view all the answers

Which dynamic programming method is used to determine the value of a state?

Value Iteration Signup and view all the answers

Are actions in an environment always reversible for the agent?

No, actions are not always reversible Signup and view all the answers

What are two typical application areas of reinforcement learning?

Game playing and robotics Signup and view all the answers

What is the typical action space of games?

Discrete Signup and view all the answers

What is the goal of reinforcement learning?

To learn a policy that maximizes the cumulative reward Signup and view all the answers

What is the primary advantage of model-based methods over model-free methods?

Higher sample efficiency Signup and view all the answers

What is a key challenge of model-based methods in high-dimensional problems?

Increased sample complexity Signup and view all the answers

What are the two primary components of the dynamics model?

Transition function and reward function Signup and view all the answers

Which of the following is NOT a deep model-based approach?

Deep Q-Networks Signup and view all the answers

What is the primary goal of the planning step in model-based reinforcement learning?

Improve policy πϕ Signup and view all the answers

In the PlaNet algorithm, what is the role of the learned model?

To simulate and plan actions Signup and view all the answers

What is the outcome of combining probabilistic models and planning in the PlaNet algorithm?

Effective learning in high-dimensional environments Signup and view all the answers

Do model-based methods generally achieve better sample complexity than model-free methods?

No Signup and view all the answers

What is the core problem in deep learning?

Optimizing the network parameters to minimize a loss function Signup and view all the answers

What is the purpose of the gradient descent algorithm in deep learning?

To iteratively update the network parameters in the direction of the negative gradient of the loss Signup and view all the answers

What is end-to-end learning in deep learning?

A training approach where raw input data is directly mapped to the desired output through a single, integrated process Signup and view all the answers

What is characteristic of large, high-dimensional problems in deep learning?

Vast and complex state and action spaces Signup and view all the answers

What is the purpose of Atari games in deep reinforcement learning research?

To serve as a benchmark in deep reinforcement learning research Signup and view all the answers

What is characteristic of Real-Time Strategy (RTS) games?

Larger state and action spaces, requiring sophisticated AI techniques Signup and view all the answers

What do deep value-based agents use to approximate value functions?

Deep learning Signup and view all the answers

What is a challenge in deep learning, apart from overfitting and vanishing gradients?

Stability and convergence during training Signup and view all the answers

What is the primary benefit of end-to-end planning and learning?

It improves sample efficiency and planning. Signup and view all the answers

What are two examples of end-to-end planning and learning methods?

Dreamer and PlaNet Signup and view all the answers

Why are model-based methods used?

They can achieve higher sample efficiency. Signup and view all the answers

What does the 'Model' refer to in model-based methods?

A representation of the environment's dynamics. Signup and view all the answers

What is the key difference between model-free and model-based methods?

Model-free methods do not use a model, while model-based methods do. Signup and view all the answers

What is the primary advantage of using model-based methods?

They achieve higher sample efficiency. Signup and view all the answers

What is Dyna?

A hybrid approach that combines model-free and model-based learning. Signup and view all the answers

What is the difference between planning and learning?

The correct answer is not provided in the text. Signup and view all the answers

Study Notes

Reinforcement Learning Basics

The discount factor (γ) is less emphasized in episodic problems with clear termination.
"Model-free" refers to methods that don't use a model of the environment's dynamics (e.g., Q-learning).
"Model-based" refers to methods that use a model of the environment to make decisions (e.g., value iteration).

Value-Based Methods

Value-based methods are suited for discrete action spaces and environments where state and action spaces are not excessively large.
Value-based methods are used for games because they often have discrete action spaces and clearly defined rules.

Gym Environments

Two basic Gym environments are Mountain Car and Cartpole.

Biological Name of RL

The biological name of RL is Operant Conditioning.

RL Application

RL is applied to sequential decision-making problems.
Defining a reward function that accurately reflects long-term objectives without unintended side effects is the main problem of assigning reward.

MDP Elements

The five MDP elements are States (S), Actions (A), Transition probabilities (Ta), Rewards (Ra), and Discount factor (γ).
Agent: Actions, Policy; Environment: States, Transition probabilities, Rewards, Discount factor.

Q(s, a)

Q(s, a) is the state-action value, representing the expected cumulative reward starting from state s, taking action a, and following policy π.

Dynamic Programming

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems, using the principle of optimality.

Recursion

Recursion is a method of solving problems where the solution depends on solutions to smaller instances of the same problem.

Value Iteration

Value iteration is a dynamic programming method used to determine the value of a state.

Typical Application Areas of RL

Two typical application areas of RL are game playing (e.g., chess, Go) and robotics (e.g., robotic control).

Action Space and Environment

The action space of games is typically discrete, while the action space of robots is typically continuous.
The environment of games can be either deterministic or stochastic, but many classic board games have deterministic environments.
The environment of robots is typically stochastic due to the unpredictability of real-world conditions.

Goal of RL

The goal of RL is to learn a policy that maximizes the cumulative reward.

Core Problem in Deep Learning

The main challenge in deep learning is to train deep neural networks effectively to generalize well on unseen data.

Gradient Descent

Gradient Descent is a key optimization algorithm used in deep learning to minimize the loss function by iteratively updating the network parameters in the direction of the negative gradient of the loss.

End-to-end Learning

End-to-end Learning is a training approach where raw input data is directly mapped to the desired output through a single, integrated process, typically using deep neural networks.

Large, High-Dimensional Problems

Large, high-dimensional problems are characterized by vast and complex state and action spaces, common in applications such as video games and real-time strategy games.

Atari Arcade Games

Atari Games serve as a benchmark in deep reinforcement learning research, presenting a variety of tasks that are challenging for AI due to their high-dimensional state spaces and complex dynamics.

Real-Time Strategy and Video Games

Real-Time Strategy (RTS) Games involve managing resources, strategic planning, and real-time decision-making, making them more complex than arcade games.

Deep Value-Based Agents

Deep value-based agents use deep learning to approximate value functions, enabling them to handle large and high-dimensional state spaces.

Model-Based Learning and Planning

Model-based learning and planning involves learning a model of the environment's dynamics and using this model for planning and decision-making.

Hands On: PlaNet Example

PlaNet Example is a detailed example using the PlaNet algorithm, which combines probabilistic models and planning for effective learning in high-dimensional environments.

Advantage of Model-Based Methods

Model-based methods can achieve higher sample efficiency by using a learned model to simulate and plan actions, reducing the need for extensive interactions with the real environment.

Sample Complexity of Model-Based Methods

In high-dimensional problems, accurately learning the transition model requires a large number of samples, which can lead to increased sample complexity.

Functions of the Dynamics Model

The dynamics model typically includes the transition function T(s, a) = s' and the reward function R(s, a).

Deep Model-Based Approaches

Four deep model-based approaches are PlaNet, Model-Predictive Control (MPC), World Models, and Dreamer.

End-to-End Planning and Learning

End-to-end planning and learning can jointly optimize model learning and policy learning, leading to better integration and performance.

Dreamer and PlaNet

Two end-to-end planning and learning methods are Dreamer and PlaNet.

Model-Based Methods

Model-based methods are used because they can achieve higher sample efficiency by learning and utilizing a model of the environment's dynamics, which allows for better planning and decision-making with fewer interactions with the real environment.

The “Model”

The “Model” refers to a representation of the environment's dynamics, typically including a transition function that predicts future states based on current states and actions, and a reward function that predicts the rewards received.

Difference between Model-Free and Model-Based

Model-free methods learn policies or value functions directly from experience without explicitly modeling the environment, whereas model-based methods first learn a model of the environment's dynamics and use this model for planning and decision-making.

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