Chapter 2 - Hard

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Questions and Answers

In reinforcement learning, what is a potential problem when the agent can choose which training examples are generated?

The agent might spend too much time exploring suboptimal parts of the state space

What is the primary goal of an agent in a Grid world environment?

To navigate a rectangular grid to reach a goal while avoiding obstacles

What are the five elements necessary to model reinforcement learning problems using MDPs?

States, Actions, Transition probabilities, Rewards, and Discount factor

In a tree diagram, what direction is successor selection of behavior?

Down Signup and view all the answers

What is the direction of learning values through backpropagation in a tree diagram?

Up Signup and view all the answers

What is a sequence of state-action pairs called in reinforcement learning?

A trace Signup and view all the answers

What is the expected cumulative reward starting from state s and following policy π represented by?

V(s) Signup and view all the answers

What is the method of solving complex problems by breaking them down into simpler subproblems using the principle of optimality called?

Dynamic programming Signup and view all the answers

What type of environment requires trajectory planning?

Mazes Signup and view all the answers

What is the goal of the agent in a grid world?

To find the sequence of actions to reach the goal state Signup and view all the answers

What is the role of the environment in an agent-environment interaction?

To provide states, rewards, and transitions based on the agent's actions Signup and view all the answers

What is the definition of an irreversible environment action?

An action that cannot be undone once taken Signup and view all the answers

What is the purpose of the discount factor γ in an MDP?

To discount future rewards Signup and view all the answers

What is the difference between a deterministic and stochastic environment?

Deterministic environments are predictable, while stochastic environments are not Signup and view all the answers

What type of action space is characterized by a limited number of actions?

Discrete action space Signup and view all the answers

What is the 5-tuple that defines a Markov Decision Process (MDP)?

(S, A, Ta, Ra, γ) Signup and view all the answers

What is the characteristic of Monte Carlo methods in terms of bias and variance?

High variance and low bias Signup and view all the answers

What type of learning updates policy based on the actions taken by the current policy?

On-Policy SARSA Signup and view all the answers

What is the purpose of Reward Shaping in Reinforcement Learning?

To modify the reward function to make learning easier Signup and view all the answers

What is the main goal of Bandit Theory in Reinforcement Learning?

To maximize rewards with minimal trials Signup and view all the answers

What is the role of -greedy Exploration in Reinforcement Learning?

To introduce randomness in action selection to ensure exploration Signup and view all the answers

What is the characteristic of Temporal Difference methods in terms of bias and variance?

Low variance and high bias Signup and view all the answers

What type of learning updates policy based on the best possible actions?

Off-Policy Q-Learning Signup and view all the answers

What is the name of the scenario where rewards are given only at specific states, making learning more difficult?

Sparse Rewards Signup and view all the answers

What is the primary characteristic of the recursion method?

Solving problems using solutions to smaller instances of the same problem 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

What is a key characteristic of actions in some environments?

They are sometimes reversible Signup and view all the answers

Which of the following is NOT a typical application area of reinforcement learning?

Natural language processing Signup and view all the answers

What is the typical nature of the action space in games?

Discrete Signup and view all the answers

What is the typical nature of the environment in robots?

Stochastic Signup and view all the answers

What is the primary goal of reinforcement learning?

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

What is meant by the term 'model-free' in reinforcement learning?

Methods that do not use a model of the environment's dynamics Signup and view all the answers

What is the primary limitation of using value-based methods in reinforcement learning?

They are not suitable for environments with continuous action spaces Signup and view all the answers

Why are policy-based methods more suitable for robotics than value-based methods?

Policy-based methods can handle continuous action spaces Signup and view all the answers

What is the main challenge in designing a reward function in reinforcement learning?

Defining a reward function that accurately reflects long-term objectives without unintended side effects Signup and view all the answers

What is the name of the equation that relates the value function of a state to the value functions of its successor states?

Bellman Equation Signup and view all the answers

What is the term for methods that allow the agent to learn directly from raw experience without a model of the environment dynamics?

Model-free methods Signup and view all the answers

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

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

What is the name of the algorithm that computes the value function using the Bellman Equation?

Value Iteration Signup and view all the answers

What is the term for the interaction between the agent and the environment in reinforcement learning?

RL Interaction Signup and view all the answers

Study Notes

Grid Worlds, Mazes, and Box Puzzles

Examples of environments where an agent navigates to reach a goal
Goal: Find the sequence of actions to reach the goal state from the start state

Grid Worlds

A rectangular grid where the agent moves to reach a goal while avoiding obstacles

Mazes and Box Puzzles

Complex environments requiring trajectory planning
Box Puzzles (e.g., Sokoban): Puzzles where the agent pushes boxes to specific locations, with irreversible actions

Tabular Value-Based Agents

Agent and Environment

Agent: Learns from interacting with the environment
Environment: Provides states, rewards, and transitions based on the agent’s actions
Interaction: The agent takes actions, receives new states and rewards, and updates its policy based on the rewards received

Markov Decision Process (MDP)

Defined as a 5-tuple (S, A, Ta, Ra, γ)
S: Finite set of states
A: Finite set of actions
Ta: Transition probabilities between states
Ra: Reward function for state transitions
γ: Discount factor for future rewards

State S

Representation: The configuration of the environment
Types:
- Deterministic Environment: Each action leads to a specific state
- Stochastic Environment: Actions can lead to different states based on probabilities

State Representation

Description: How states are defined and represented in the environment

Action A

Types:
- Discrete: Finite set of actions (e.g., moving in a grid)
- Continuous: Infinite set of actions (e.g., robot movements)

Irreversible Environment Action

Definition: Actions that cannot be undone once taken

Exploration

Bandit Theory: Balances exploration and exploitation
-greedy Exploration: Chooses a random action with probability , and the best-known action with probability 1-

Off-Policy Learning

On-Policy SARSA: Updates policy based on the actions taken by the current policy
Off-Policy Q-Learning: Updates policy based on the best possible actions, not necessarily those taken by the current policy

Q-Learning

Description: Updates value estimates based on differences between successive state values

Temporal Difference Learning

Description: Updates value estimates based on differences between successive state values

Monte Carlo Sampling

Description: Generates random episodes and uses returns to update the value function

Bias-Variance Trade-off

Monte Carlo methods have high variance and low bias, while temporal difference methods have low variance and high bias

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