Chapter 6 - Hard

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Questions and Answers

What is the main purpose of the Upper Confidence bounds applied to Trees (UCT) policy in Monte Carlo Tree Search?

To guide the selection and expansion steps in the search (correct)

To balance the exploration and exploitation of actions

To optimize the neural network's prior probabilities

To reduce the exploration rate in the simulation

What is the main difference between the UCT and P-UCT policies in Monte Carlo Tree Search?

UCT uses prior probabilities from a neural network, while P-UCT does not

P-UCT uses prior probabilities from a neural network, while UCT does not (correct)

UCT is used for exploitation, while P-UCT is used for exploration

UCT is used for games like Go and Chess, while P-UCT is used for Shogi

What is the purpose of the Policy Network in the context of Monte Carlo Tree Search?

To approximate the policy distribution over actions (correct)

To balance exploration and exploitation

To approximate the value function

To regularize the learning process

What is the goal of the Backpropagation step in the Monte Carlo Tree Search algorithm?

To update the values of the nodes Signup and view all the answers

What is the main application of Monte Carlo Tree Search?

Game playing, particularly in games like Go and Chess Signup and view all the answers

What is the purpose of the Value Network in the context of Monte Carlo Tree Search?

To approximate the value function Signup and view all the answers

What is the main challenge addressed by the concept of Exploration/Exploitation in Monte Carlo Tree Search?

Balancing the exploration of new actions with the exploitation of known rewarding actions Signup and view all the answers

What is the main goal of the Simulation step in the Monte Carlo Tree Search algorithm?

To simulate the outcome of an action Signup and view all the answers

What is the primary benefit of using curriculum learning in agents?

Faster learning speed and improved generalization Signup and view all the answers

What is the key difference between AlphaGo and AlphaGo Zero?

AlphaGo used human data, while AlphaGo Zero learned from self-play Signup and view all the answers

What is the primary function of the policy network in AlphaGo Zero?

Selecting the next move in a game Signup and view all the answers

What is the main idea behind self-play in AlphaGo Zero?

Learning by playing against itself Signup and view all the answers

What is the estimated size of the state space in Go?

10^170 Signup and view all the answers

What is the primary goal of the UCT formula in MCTS?

Balancing exploration and exploitation Signup and view all the answers

What is the main purpose of backpropagation in MCTS?

Updating the tree with new information Signup and view all the answers

What is the primary advantage of using MCTS in game-playing agents?

Ability to handle large state spaces efficiently Signup and view all the answers

What is the primary difference between AlphaGo and AlphaGo Zero?

The use of human data in training Signup and view all the answers

How does UCT balance exploration and exploitation in MCTS?

By combining known rewards with exploration of less-visited actions Signup and view all the answers

What is the purpose of the Backpropagation step in MCTS?

To update the estimated values of nodes Signup and view all the answers

How does P-UCT differ from UCT in selecting actions?

P-UCT incorporates prior probabilities from a neural network Signup and view all the answers

What is the primary goal of the Expansion step in MCTS?

To create new child nodes Signup and view all the answers

What is the main advantage of using self-play in two-agent zero-sum games?

Reduced need for human data Signup and view all the answers

What is the purpose of the Simulation step in MCTS?

To simulate playouts to estimate outcomes Signup and view all the answers

How does AlphaZero generalize the approach of AlphaGo Zero?

By applying self-play to Chess and Shogi Signup and view all the answers

What is the primary purpose of backpropagation in the Monte Carlo Tree Search algorithm?

To update the values of all nodes on the path from the leaf to the root based on the simulation result Signup and view all the answers

How does the UCT algorithm balance exploration and exploitation?

By using a formula that balances the average reward with the exploration term Signup and view all the answers

What happens when Cp is small in MCTS?

MCTS tends to exploit more Signup and view all the answers

What is the advantage of tabula rasa learning over reinforcement learning on top of supervised learning of grandmaster games?

Tabula rasa learning is faster because it avoids the constraints of biased data and explores the search space more freely Signup and view all the answers

What is the primary difference between a double-headed network and a regular actor-critic architecture?

The presence of two output heads for policy and value Signup and view all the answers

What are the three elements that make up the self-play loop?

Self-Play, Training the Neural Network, and Updating the Policy Signup and view all the answers

What is the primary goal of curriculum learning?

To gradually increase the complexity of the learning task Signup and view all the answers

What is the primary advantage of using a simulation in MCTS?

To obtain an outcome and update the values of the nodes Signup and view all the answers

What is the primary goal of Curriculum Learning in the context of self-play?

To improve the agent's performance by gradually increasing the difficulty of tasks Signup and view all the answers

What is the main advantage of combining Supervised and Reinforcement Learning in a curriculum?

To leverage the strengths of both methods Signup and view all the answers

What is the primary function of Procedural Content Generation?

To automatically generate tasks or environments Signup and view all the answers

What is the main difference between Single-Agent Curriculum Learning and Curriculum Learning?

Single-Agent Curriculum Learning is a variant of Curriculum Learning applied to a single-agent context Signup and view all the answers

What is the primary contribution of AlphaGo Zero?

It learned to play Go from scratch using self-play Signup and view all the answers

What is the primary purpose of the Open Self-Play Frameworks?

To provide open-source tools for developing self-play agents Signup and view all the answers

What is the main difference between AlphaGo Zero and AlphaZero?

AlphaZero is a generalization of AlphaGo Zero, while AlphaGo Zero is limited to Go Signup and view all the answers

What is the primary focus of the Hands On: Hex in Polygames Example?

To create a self-play agent for the game of Hex Signup and view all the answers

Study Notes

Monte Carlo Tree Search (MCTS)

MCTS is a search algorithm that balances exploration and exploitation using random sampling of the search space
It consists of four steps: Selection, Expansion, Simulation, and Backpropagation
Selection: selects the optimal child node recursively until a leaf node is reached
Expansion: adds one or more child nodes to the leaf node if it is not terminal
Simulation: runs a simulation from the new nodes to obtain an outcome
Backpropagation: updates the values of all nodes on the path from the leaf to the root based on the simulation result

Upper Confidence bounds applied to Trees (UCT)

UCT is a policy used in MCTS to select actions
It balances the average reward (exploitation) with the exploration term that favors less-visited actions
Formula: UCT = Q(s, a) + c * sqrt(ln N(s) / N(s, a))
P-UCT is a variant of UCT that incorporates prior probabilities from a neural network

Self-Play

Self-play is a training method where an agent learns by playing against itself
It consists of three levels: move-level, example-level, and tournament-level self-play
Example-level self-play involves training a policy and value network using neural networks
Tournament-level self-play involves training the agent on a sequence of tasks of increasing difficulty

Curriculum Learning

Curriculum learning is a method where an agent learns tasks in a sequence of increasing difficulty
It helps in better generalization and faster learning
Algorithm: Initialize curriculum C with tasks of increasing difficulty, train agent on each task using self-play

AlphaGo and AlphaZero

AlphaGo used supervised learning from human games and reinforcement learning
AlphaGo Zero learned purely from self-play without human data
AlphaZero is a generalization of AlphaGo Zero that achieved superhuman performance in Chess, Shogi, and Go
AlphaZero uses a neural network and MCTS to learn from self-play

Other Concepts

Tabula rasa learning: learning from scratch without any prior knowledge or data
Double-headed network: a neural network with two output heads, one for policy and one for value
Minimax: a decision rule used for minimizing the possible loss for a worst-case scenario in zero-sum games

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