Chapter 2 - Medium

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Questions and Answers

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

To push boxes to specific locations

To navigate to a goal while avoiding obstacles (correct)

To maximize rewards in a stochastic environment

To find the shortest path to a goal state

What is the purpose of the discount factor γ in a Markov Decision Process (MDP)?

To calculate the expected future rewards (correct)

To define the set of possible actions

To determine the next state in a stochastic environment

To specify the reward function for state transitions

What is the main difference between a deterministic and stochastic environment?

The complexity of the environment

The number of possible actions

The predictability of outcomes for actions (correct)

The type of reward function used

What is an irreversible environment action?

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

What is the purpose of the transition probabilities Ta in a Markov Decision Process (MDP)?

To model the uncertainty of state transitions Signup and view all the answers

What is the main characteristic of a box puzzle?

The agent pushes boxes to specific locations Signup and view all the answers

What is the purpose of the state representation in a Markov Decision Process (MDP)?

To define and represent the states in the environment Signup and view all the answers

What is the main difference between a discrete and continuous action space?

The number of possible actions Signup and view all the answers

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

Low bias and high variance Signup and view all the answers

What is the difference between On-Policy SARSA and Off-Policy Q-Learning?

On-Policy SARSA updates policy based on the current policy, while Off-Policy Q-Learning updates policy based on the best possible actions 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 characteristic of Temporal Difference methods in terms of bias and variance?

High bias and low variance Signup and view all the answers

What is the purpose of -greedy Exploration in reinforcement learning?

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

What is the purpose of the Q-Table in Q-Learning?

To initialize Q-values for all state-action pairs Signup and view all the answers

What is the main advantage of the agent being able to choose its training examples in reinforcement learning?

It allows the agent to explore different parts of the state space Signup and view all the answers

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 Signup and view all the answers

What are the five essential elements of an MDP?

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

What direction does the successor selection of behavior occur in a tree diagram?

Down Signup and view all the answers

What direction does the learning values through backpropagation occur in a tree diagram?

Up Signup and view all the answers

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

τ Signup and view all the answers

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

V(s) Signup and view all the answers

What is the method for solving complex problems by breaking them down into simpler subproblems?

Dynamic programming Signup and view all the answers

What is the primary approach to problem-solving used in recursion?

Dividing the problem into smaller instances Signup and view all the answers

What is the main purpose of value iteration?

To determine the value of a state Signup and view all the answers

Which of the following environments may have irreversible actions?

Robotics environments 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 type of action space does robotics typically have?

Continuous Signup and view all the answers

What is the primary characteristic of the environment in games?

Deterministic 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

Which concept is less emphasized in episodic problems?

Discount factor Signup and view all the answers

What type of action space is suited for value-based methods?

Discrete action spaces Signup and view all the answers

Why are value-based methods used for games?

Games often have discrete action spaces and clearly defined rules Signup and view all the answers

What are two basic Gym environments?

Mountain Car and Cartpole Signup and view all the answers

What is the biological name of Reinforcement Learning?

Operant Conditioning Signup and view all the answers

What are the two central elements of Reinforcement Learning Interaction?

Agent and Environment Signup and view all the answers

What is the main problem of assigning reward?

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 recursion relation central to the value function?

Bellman Equation Signup and view all the answers

What is the characteristic of model-free methods?

Learning directly from raw experience without a model of the environment dynamics 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

Studying That Suits You

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Description

Navigate through grid worlds, mazes, and box puzzles to reach a goal state while avoiding obstacles and planning trajectories.