### Install Connect Four Solver and Book

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/games/connect-four/solver/README.md

This snippet guides users through cloning the Pascal Pons' connect4 solver repository, navigating into its directory, and downloading the necessary 7x6 book file for benchmarking. This enables comparison with a perfect Connect Four solver.

```shell
git clone https://github.com/PascalPons/connect4
cd connect4
wget https://github.com/PascalPons/connect4/releases/download/book/7x6.book
```

--------------------------------

### MCTS Rollout Oracle and Environment Setup in Julia

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This snippet demonstrates how to set up a Monte Carlo Tree Search (MCTS) environment using a rollout-based oracle in AlphaZero.jl. It covers initialization of the oracle and environment, running simulations, retrieving the policy and statistics, and resetting the environment for a new game. Dependencies include the AlphaZero.MCTS module.

```julia
using AlphaZero

# Create rollout-based oracle (vanilla MCTS)
oracle = AlphaZero.MCTS.RolloutOracle(game_spec, 1.0)

# Create MCTS environment
mcts_env = AlphaZero.MCTS.Env(
    game_spec,
    oracle,
    gamma=1.0,
    cpuct=1.0,
    noise_ϵ=0.25,
    noise_α=1.0)

# Run MCTS simulations
AlphaZero.MCTS.explore!(mcts_env, game, 100)

# Get recommended policy
actions, policy = AlphaZero.MCTS.policy(mcts_env, game)
println("MCTS policy: ", Dict(zip(actions, policy)))

# Get statistics
depth = AlphaZero.MCTS.average_exploration_depth(mcts_env)
memory = AlphaZero.MCTS.approximate_memory_footprint(mcts_env)
println("Average depth: $depth, Memory: $(memory ÷ 1024)KB")

# Reset for new game
AlphaZero.MCTS.reset!(mcts_env)
```

--------------------------------

### Execute Training Script via Command Line

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/scripts.md

This script demonstrates how to initiate or resume a training session for a specified game (e.g., 'connect-four') using the AlphaZero.Scripts module from the command line. It requires the 'AlphaZero' package to be installed and the project environment to be active. The first argument specifies the experiment to load, which can be an Experiment object or a string key from AlphaZero.Examples.experiment.

```sh
julia --project -e 'using AlphaZero; Scripts.train("connect-four")'
```

--------------------------------

### Configure AlphaZero Training Session in Julia

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

Details the configuration and initiation of a complete AlphaZero training session in Julia. This snippet shows how to load a predefined game, create a neural network (ResNet in this case), and implies the subsequent steps for training setup.

```julia
using AlphaZero

# Load a predefined game
game_spec = AlphaZero.Examples.games["connect-four"]

# Create neural network
network = AlphaZero.NetLib.ResNet(game_spec,
    AlphaZero.NetLib.ResNetHP(
        num_filters=128,
        num_blocks=5,
        conv_kernel_size=(3,3),
        num_policy_head_filters=32,
        num_value_head_filters=32,
        batch_norm_momentum=0.1))
```

--------------------------------

### Configure and Train AlphaZero Agent

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This snippet shows how to configure training parameters for AlphaZero, create a training environment, and start the training process. It also demonstrates how to create a trained player and generate an initial report.

```julia
params = AlphaZero.Params(
    num_iters=25,
    use_symmetries=true,
    mem_buffer_size=AlphaZero.PLSchedule(
        [0, 15], [400_000, 1_000_000]),
    self_play=AlphaZero.SelfPlayParams(
        mcts=AlphaZero.MctsParams(
            num_iters_per_turn=600,
            cpuct=1.0,
            temperature=AlphaZero.StepSchedule([1.0, 0.5, 0.3], [20, 30]),
            dirichlet_noise_ϵ=0.25,
            dirichlet_noise_α=0.3),
        sim=AlphaZero.SimParams(
            num_games=5000,
            num_workers=128,
            batch_size=128,
            use_gpu=true,
            reset_every=2)),
    learning=AlphaZero.LearningParams(
        use_gpu=true,
        samples_weighing_policy=AlphaZero.LOG_WEIGHT,
        batch_size=2048,
        loss_computation_batch_size=2048,
        optimiser=AlphaZero.CyclicNesterov(
            lr_base=0.2,
            lr_high=0.6,
            lr_low=0.01,
            momentum_low=0.9,
            momentum_high=0.99),
        l2_regularization=1e-4,
        min_checkpoints_per_epoch=1,
        max_batches_per_checkpoint=1000,
        num_checkpoints=4),
    arena=AlphaZero.ArenaParams(
        mcts=AlphaZero.MctsParams(
            num_iters_per_turn=600,
            cpuct=1.0,
            temperature=AlphaZero.ConstSchedule(0.2),
            dirichlet_noise_ϵ=0.0,
            dirichlet_noise_α=0.3),
        sim=AlphaZero.SimParams(
            num_games=100,
            num_workers=128,
            batch_size=128,
            use_gpu=true),
        update_threshold=0.0))

env = AlphaZero.Env(game_spec, params, network)
report = AlphaZero.initial_report(env)
println("Network has $(report.num_network_parameters) parameters")

AlphaZero.train!(env)

trained_player = AlphaZero.AlphaZeroPlayer(env, timeout=2.0)
```

--------------------------------

### Distributed Training Setup in Julia with AlphaZero

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This snippet illustrates how to set up distributed training for AlphaZero agents across multiple machines using Julia's `Distributed` module. It covers adding worker processes, loading necessary packages and game/experiment configurations on all workers, and initiating the training process. The `Env` and `train!` functions automatically leverage available workers for distributed simulations. The `SimParams` control the degree of parallelization.

```julia
using AlphaZero
using Distributed

# Add worker processes (can be on different machines)
addprocs(4)

@everywhere using AlphaZero

# Load game and experiment on all workers
game_spec = AlphaZero.Examples.games["connect-four"]
experiment = AlphaZero.Examples.experiments["connect-four"]

# Training automatically uses all available workers
# No code changes needed - distributed simulation happens automatically
env = AlphaZero.Env(
    game_spec,
    experiment.params,
    experiment.mknet(game_spec, experiment.netparams))

# Self-play simulations automatically distributed across workers
AlphaZero.train!(env)

# The SimParams controls parallelization
# num_workers: parallel game simulators
# batch_size: neural network inference batch size
# Simulations distributed across processes via Distributed.@spawn
```

--------------------------------

### Run OpenSpiel TTT Experiment with AlphaZero.jl (Julia)

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/games/ospiel_ttt/README.md

This snippet shows how to instantiate and run an AlphaZero.jl experiment using the OpenSpiel wrapper for Tic-Tac-Toe. It requires the AlphaZero and OpenSpiel packages to be installed. The output is a trained AlphaZero session.

```julia
using AlphaZero
import OpenSpiel # load additional features through Requires.jl

ospiel_experiment = Examples.experiments["ospiel_ttt"]
session = Session(ospiel_experiment, dir="sessions/ospiel_ttt")

resume!(session)
```

--------------------------------

### Train Connect Four Agent with AlphaZero.jl

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/README.md

This snippet demonstrates how to clone the AlphaZero.jl repository, set up the environment, and train a Connect Four agent using the provided scripts. It requires Git and Julia to be installed.

```shell
export GKSwstype=100  # To avoid an occasional GR bug
git clone https://github.com/jonathan-laurent/AlphaZero.jl.git
cd AlphaZero.jl
julia --project -e 'import Pkg; Pkg.instantiate()'
julia --project -e 'using AlphaZero; Scripts.train("connect-four")'
```

--------------------------------

### Train OpenSpiel TTT Experiment with AlphaZero.jl (Shell)

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/games/ospiel_ttt/README.md

This command-line snippet provides a simpler way to train an AlphaZero.jl experiment with the OpenSpiel wrapper for Tic-Tac-Toe using Julia's project mode. It requires AlphaZero and OpenSpiel to be installed. The output is a trained AlphaZero session.

```sh
julia --project -e 'using AlphaZero; import OpenSpiel; Scripts.train("ospiel_ttt")'
```

--------------------------------

### Play AlphaZero Agent Interactively

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/tutorial/connect_four.md

Starts an interactive session to play against the current AlphaZero agent without complex metrics. This is a simplified interface for direct gameplay.

```julia
using AlphaZero; Scripts.play("connect-four")
```

--------------------------------

### Benchmarking and Evaluating AlphaZero Agents in Julia

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This code demonstrates how to evaluate and benchmark trained AlphaZero agents against baseline players (random and MCTS) in Julia. It sets up game specifications, loads trained agents, defines baseline players, and uses a custom evaluation function and the built-in Benchmark module to run comparisons. Key dependencies include AlphaZero and its submodules like Examples, UserInterface, MCTS, and Benchmark.

```julia
using AlphaZero

# Create game and players
game_spec = AlphaZero.Examples.games["connect-four"]

# Trained AlphaZero player
experiment = AlphaZero.Examples.experiments["connect-four"]
session = AlphaZero.UserInterface.Session(experiment, dir="sessions/connect-four")
az_player = AlphaZero.AlphaZeroPlayer(session.env)

# Random baseline
random_player = AlphaZero.RandomPlayer()

# MCTS baseline with rollouts
rollout_oracle = AlphaZero.MCTS.RolloutOracle(game_spec)
mcts_baseline = AlphaZero.MctsPlayer(
    AlphaZero.MCTS.Env(game_spec, rollout_oracle, cpuct=1.0),
    AlphaZero.ConstSchedule(0.5),
    niters=100)

# Play games
function evaluate_players(player1, player2, num_games)
    wins = 0
    for i in 1:num_games
        game = AlphaZero.GI.init(game_spec)
        players = AlphaZero.TwoPlayers(player1, player2)
        trace = AlphaZero.play_game(game_spec, players)
        reward = AlphaZero.total_reward(trace, gamma=1.0)
        wins += (reward > 0) ? 1 : 0
    end
    return wins / num_games
end

win_rate = evaluate_players(az_player, random_player, 100)
println("Win rate vs random: $(win_rate * 100)%")

# Use built-in benchmark
benchmark = AlphaZero.Benchmark.Benchmark([
    AlphaZero.Benchmark.Duel(az_player, random_player, num_games=200),
    AlphaZero.Benchmark.Duel(az_player, mcts_baseline, num_games=200)
])

results = AlphaZero.Benchmark.run(benchmark, game_spec)
```

--------------------------------

### Create and Use MCTS Player with Neural Network in Julia

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

Illustrates the creation and usage of an MCTS player in Julia, which utilizes a neural network oracle. It covers initializing the game, creating a neural network, defining MCTS parameters, and selecting moves. The example uses the 'connect-four' game spec and a SimpleNet.

```julia
using AlphaZero

# Create game specification
game_spec = AlphaZero.Examples.games["connect-four"]

# Create a simple neural network
network = AlphaZero.NetLib.SimpleNet(game_spec,
    AlphaZero.NetLib.SimpleNetHP(
        width=128,
        depth_common=4,
        use_batch_norm=true,
        batch_norm_momentum=0.1))

# Define MCTS parameters
mcts_params = AlphaZero.MctsParams(
    num_iters_per_turn=600,
    cpuct=1.0,
    temperature=AlphaZero.ConstSchedule(1.0),
    dirichlet_noise_ϵ=0.25,
    dirichlet_noise_α=0.3,
    prior_temperature=1.0)

# Create MCTS player
player = AlphaZero.MctsPlayer(game_spec, network, mcts_params)

# Initialize game and play
game = AlphaZero.GI.init(game_spec)
action = AlphaZero.select_move(player, game, 1)
AlphaZero.GI.play!(game, action)

# Get policy distribution
actions, policy = AlphaZero.think(player, game)
println("Action probabilities: ", policy)

# Reset player (clears MCTS tree)
AlphaZero.reset_player!(player)
```

--------------------------------

### Memory Buffer and Training Sample Documentation

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/memory.md

Details regarding the TrainingSample data structure, the MemoryBuffer for storing training data, and functions to retrieve experience and push training traces.

```APIDOC
## Memory Buffer and Training Sample API

### Description
This section documents the `TrainingSample` struct, used to hold individual training data points, and the `MemoryBuffer` struct, which acts as a collection for these samples. It also covers functions for interacting with the memory buffer, such as retrieving stored experience and adding new training traces.

### Endpoints

#### TrainingSample

##### Description
Represents a single training sample, typically containing game states, action probabilities, and value estimates.

##### Type
`struct TrainingSample`

##### Fields
- **state** (Any) - The game state representation.
- **policy** (Any) - The policy vector or probability distribution over actions.
- **value** (Float64) - The estimated value of the state.

#### MemoryBuffer

##### Description
A data structure for efficiently storing and retrieving `TrainingSample` objects during reinforcement learning training.

##### Type
`struct MemoryBuffer`

##### Fields
- **trace** (Any) - Internal storage for training traces.
- **rng** (Any) - Random number generator for sampling.

#### `get_experience`

##### Description
Retrieves experience from the `MemoryBuffer`.

##### Method
`get_experience(::MemoryBuffer)

##### Parameters
- **::MemoryBuffer** (MemoryBuffer) - The memory buffer instance from which to retrieve experience.

##### Returns
(Any) - The retrieved experience data.

#### `push_trace!`

##### Description
Pushes a training trace into the `MemoryBuffer`.

##### Method
`push_trace!(::MemoryBuffer, ::Any)

##### Parameters
- **::MemoryBuffer** (MemoryBuffer) - The memory buffer instance to which the trace will be added.
- **::Any** (Any) - The training trace data to push.

##### Returns
(Nothing) - This function modifies the buffer in place and does not return a value.
```

--------------------------------

### User Interface - Explorer

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/ui.md

Documentation for the Explorer module, detailing the `explore` function for visualizing and interacting with experiments.

```APIDOC
## User Interface - Explorer

### Description
This section covers the `Explorer` functionality within the User Interface module. It provides tools for exploring and visualizing experimental results.

### Methods & Types
- **`explore`**: Function to launch the explorer interface, likely for visualizing experiment data and progress.
```

--------------------------------

### AlphaZero Utility Functions

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/omitted.md

Documentation for general utility functions within the AlphaZero.Util module.

```APIDOC
## AlphaZero.Util API

### Description
Provides various utility functions for the AlphaZero project.

### Method
N/A (Documentation of types and functions)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Utilities Documentation

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Provides documentation for utility functions related to sample management and scheduling.

```APIDOC
## Utilities

### Description
This section documents utility functions for managing necessary samples and different scheduling policies.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Wrapper for CommonRLInterface.jl

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Documentation for the wrapper that integrates AlphaZero.jl with the CommonRLInterface.jl.

```APIDOC
## Wrapper for CommonRLInterface.jl

### Description
This section describes the wrapper provided by AlphaZero.jl to interface with the CommonRLInterface.jl standard.

### Types
- **`CommonRLInterfaceWrapper`**: The main wrapper type.
- **`CommonRLInterfaceWrapper.Env`**: Represents the environment within the wrapper.
- **`CommonRLInterfaceWrapper.Spec`**: Represents the specification within the wrapper.
```

--------------------------------

### User Interface - Session

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/ui.md

Documentation for the Session module, including constructors, resume, save, and reporting functionalities for experiments.

```APIDOC
## User Interface - Session

### Description
This section covers the `Session` functionality within the User Interface module. It includes details on creating, resuming, saving, and reporting on experimental sessions.

### Methods & Types
- **`Session`**: Represents an experimental session. Includes constructors like `Session(::Experiment)` which provides access to all constructors for a given experiment.
- **`resume!`**: Function to resume a saved session.
- **`save`**: Function to save the current state of a session.
- **`SessionReport`**: A type to hold reports generated from a session.
```

--------------------------------

### General Training Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Provides documentation for the general training parameters structure.

```APIDOC
## General Training Parameters

### Description
This section details the general training parameters used in the AlphaZero project.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Batchifying Oracles

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/omitted.md

Documentation related to batchifying oracle calls, including the main Batchifier, launching a server, signaling client completion, and the BatchedOracle type.

```APIDOC
## Batchifier API

### Description
Provides functionality for batching oracle calls to improve efficiency.

### Method
N/A (Documentation of types and functions)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A

## Batchifier.launch_server

### Description
Launches a server for handling batched oracle requests.

### Method
N/A

### Endpoint
N/A

## Batchifier.client_done!

### Description
Signals that a client has finished its tasks.

### Method
N/A

### Endpoint
N/A

## Batchifier.BatchedOracle

### Description
A type representing an oracle that handles batched requests.

### Method
N/A

### Endpoint
N/A
```

--------------------------------

### Arena Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Explains the parameters for configuring the arena used for evaluating agents.

```APIDOC
## Arena Parameters

### Description
This section describes the parameters used to configure the arena for agent evaluation.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Quick Training Script for Games using AlphaZero.jl

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This snippet demonstrates how to use the simplified Scripts interface in AlphaZero.jl to train game agents. It covers training from scratch, using session-based approaches for more control, and playing interactively.

```julia
using AlphaZero

# Train Connect Four from scratch
AlphaZero.Scripts.train("connect-four")

# Or use the session-based approach for more control
experiment = AlphaZero.Examples.experiments["connect-four"]
session = AlphaZero.UserInterface.Session(
    experiment,
    dir="sessions/connect-four",
    autosave=true)

# Start or resume training
AlphaZero.UserInterface.resume!(session)

# Get the trained player from the session
player = AlphaZero.AlphaZeroPlayer(session.env)

# Play interactively against the agent
AlphaZero.Scripts.play("connect-four")

# Explore the agent's decision tree visually
AlphaZero.Scripts.explore("connect-four")

# Test other built-in games
AlphaZero.Scripts.train("tictactoe")
AlphaZero.Scripts.train("grid-world")
AlphaZero.Scripts.train("mancala")
```

--------------------------------

### KnetLib and FluxLib

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/omitted.md

Documentation for neural network implementations using Knet and Flux libraries, including network types.

```APIDOC
## KnetLib Neural Networks

### Description
Documentation for neural network architectures implemented using the Knet library.

### Method
N/A

### Endpoint
N/A

#### KnetLib.KNetwork

### Description
Represents a neural network model implemented with Knet.

### Method
N/A

### Endpoint
N/A

#### KnetLib.TwoHeadNetwork

### Description
A two-headed neural network architecture using Knet.

### Method
N/A

### Endpoint
N/A

## FluxLib Neural Networks

### Description
Documentation for neural network architectures implemented using the Flux library.

### Method
N/A

### Endpoint
N/A

#### FluxLib.FluxNetwork

### Description
Represents a neural network model implemented with Flux.

### Method
N/A

### Endpoint
N/A

#### FluxLib.TwoHeadNetwork

### Description
A two-headed neural network architecture using Flux.

### Method
N/A

### Endpoint
N/A
```

--------------------------------

### MCTS Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Explains the parameters for the Monte Carlo Tree Search (MCTS) algorithm.

```APIDOC
## MCTS Parameters

### Description
This section describes the parameters used to configure the Monte Carlo Tree Search (MCTS) algorithm.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Manual Training Session with AlphaZero.jl in Julia REPL

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/tutorial/connect_four.md

This Julia code demonstrates how to manually set up and resume a training session for the Connect Four experiment using AlphaZero.jl within the Julia REPL. It loads a predefined experiment and initializes a session.

```julia
using AlphaZero
experiment = Examples.experiments["connect-four"]
session = Session(experiment, dir="sessions/connect-four")
resume!(session)
```

--------------------------------

### Documentation for AlphaZero Scripts Functions

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/scripts.md

This section lists the available documentation for various script functions within the AlphaZero.Scripts module. These functions cover a range of functionalities including testing games, training models, playing games, exploring experiments, dummy runs, and testing gradient updates.

```julia
@docs Scripts.test_game
Scripts.train
Scripts.play
Scripts.explore
```

```julia
@docs Scripts.dummy_run
Scripts.test_grad_updates
```

--------------------------------

### Clone and Train Connect Four Agent with AlphaZero.jl

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/tutorial/connect_four.md

This snippet demonstrates how to clone the AlphaZero.jl repository, navigate to its directory, instantiate the project's package environment, and initiate the training process for the Connect Four agent.

```sh
git clone --branch v0.5.4 https://github.com/jonathan-laurent/AlphaZero.jl.git
cd AlphaZero.jl
julia --project -e 'import Pkg; Pkg.instantiate()'
julia --project -e 'using AlphaZero; Scripts.train("connect-four")'
```

--------------------------------

### Self-Play Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Details the parameters specific to the self-play phase of training.

```APIDOC
## Self-Play Parameters

### Description
This section describes the parameters used to configure the self-play process in AlphaZero.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Learning Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Documents parameters related to the learning and model updating phases.

```APIDOC
## Learning Parameters

### Description
This section covers the parameters for the learning phase, including model updates and sample weighing policies.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Memory Analysis Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Documents parameters for memory analysis during training.

```APIDOC
## Memory Analysis Parameters

### Description
This section details the parameters for memory analysis within the AlphaZero training process.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Implement Custom Game Interface in Julia

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

Demonstrates how to define a custom game by implementing the AbstractGameSpec and AbstractGameEnv types in Julia. This involves defining game rules, state representation, actions, and rewards, allowing integration with the AlphaZero framework.

```julia
using AlphaZero

# Define game specification
struct MyGameSpec <: AlphaZero.GI.AbstractGameSpec end

# Define game environment
mutable struct MyGameEnv <: AlphaZero.GI.AbstractGameEnv
    board::Matrix{Int}
    current_player::Int
    finished::Bool
end

# Implement required interface methods
AlphaZero.GI.spec(env::MyGameEnv) = MyGameSpec()

AlphaZero.GI.init(spec::MyGameSpec) = MyGameEnv(zeros(Int, 7, 6), 1, false)

AlphaZero.GI.two_players(::MyGameSpec) = true

AlphaZero.GI.actions(::MyGameSpec) = collect(1:7)

AlphaZero.GI.set_state!(env::MyGameEnv, state) = begin
    env.board = state.board
    env.current_player = state.current_player
    env.finished = state.finished
end

AlphaZero.GI.current_state(env::MyGameEnv) = (board=env.board, current_player=env.current_player, finished=env.finished)

AlphaZero.GI.game_terminated(env::MyGameEnv) = env.finished

AlphaZero.GI.white_playing(env::MyGameEnv) = env.current_player == 1

AlphaZero.GI.actions_mask(env::MyGameEnv) = [col <= 7 for col in 1:7]

AlphaZero.GI.play!(env::MyGameEnv, action) = begin
    # Update game state based on action
    env.current_player = 3 - env.current_player
end

AlphaZero.GI.white_reward(env::MyGameEnv) = env.finished ? 1.0 : 0.0

AlphaZero.GI.vectorize_state(::MyGameSpec, state) = Float32.(state.board)

# Optional: Define symmetries for data augmentation
AlphaZero.GI.symmetries(::MyGameSpec, state) = Tuple{typeof(state), Vector{Int}}[]

AlphaZero.GI.render(env::MyGameEnv) = println(env.board)
```

--------------------------------

### Define AlphaZero Parameters (Julia)

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/tutorial/connect_four.md

Sets up the main parameters for the AlphaZero algorithm, including network hyperparameters, self-play configurations, arena simulations, and learning settings. It also defines the benchmark suite for evaluating different players.

```Julia
Network = NetLib.ResNet

netparams = NetLib.ResNetHP(
  num_filters=128,
  num_blocks=5,
  conv_kernel_size=(3, 3),
  num_policy_head_filters=32,
  num_value_head_filters=32,
  batch_norm_momentum=0.1)

self_play = SelfPlayParams(
  sim=SimParams(
    num_games=5000,
    num_workers=128,
    batch_size=64,
    use_gpu=true,
    reset_every=2,
    flip_probability=0.,
    alternate_colors=false),
  mcts=MctsParams(
    num_iters_per_turn=600,
    cpuct=2.0,
    prior_temperature=1.0,
    temperature=PLSchedule([0, 20, 30], [1.0, 1.0, 0.3]),
    dirichlet_noise_ϵ=0.25,
    dirichlet_noise_α=1.0))

arena = ArenaParams(
  sim=SimParams(
    num_games=128,
    num_workers=128,
    batch_size=128,
    use_gpu=true,
    reset_every=2,
    flip_probability=0.5,
    alternate_colors=true),
  mcts=MctsParams(
    self_play.mcts,
    temperature=ConstSchedule(0.2),
    dirichlet_noise_ϵ=0.05),
  update_threshold=0.05)

learning = LearningParams(
  use_gpu=true,
  use_position_averaging=true,
  samples_weighing_policy=LOG_WEIGHT,
  batch_size=1024,
  loss_computation_batch_size=1024,
  optimiser=Adam(lr=2e-3),
  l2_regularization=1e-4,
  nonvalidity_penalty=1.,
  min_checkpoints_per_epoch=1,
  max_batches_per_checkpoint=2000,
  num_checkpoints=1)

params = Params(
  arena=arena,
  self_play=self_play,
  learning=learning,
  num_iters=15,
  ternary_outcome=true,
  use_symmetries=true,
  memory_analysis=nothing,
  mem_buffer_size=PLSchedule(
  [      0,        15],
  [400_000, 1_000_000]))

mcts_baseline = Benchmark.MctsRollouts(
    MctsParams(
      arena.mcts,
      num_iters_per_turn=1000,
      cpuct=1.))

minmax_baseline = Benchmark.MinMaxTS(
  depth=5,
  τ=0.2,
  amplify_rewards=true)

alphazero_player = Benchmark.Full(arena.mcts)

network_player = Benchmark.NetworkOnly(τ=0.5)

benchmark_sim = SimParams(
  arena.sim;
  num_games=256,
  num_workers=256,
  batch_size=256,
  alternate_colors=false)

benchmark = [
  Benchmark.Duel(alphazero_player, mcts_baseline,   benchmark_sim),
  Benchmark.Duel(alphazero_player, minmax_baseline, benchmark_sim),
  Benchmark.Duel(network_player,   mcts_baseline,   benchmark_sim),
  Benchmark.Duel(network_player,   minmax_baseline, benchmark_sim)
]

experiment = Experiment("connect-four",
  GameSpec(), params, Network, netparams, benchmark)

```

--------------------------------

### Optional Interface: Interactive Tools

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Provides optional functions that enhance the user interface and interaction with the game.

```APIDOC
## Optional Interface: Interface for Interactive Tools

### Description
These functions are optional and primarily support the default user interface for better interaction.

### Functions
- **`action_string`**: Converts an action to its string representation.
- **`parse_action`**: Parses a string into an action.
- **`read_state`**: Reads the current game state.
- **`render`**: Renders the current game state for visualization.
```

--------------------------------

### Standalone MCTS Usage in AlphaZero.jl

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This snippet demonstrates how to use the Monte Carlo Tree Search (MCTS) algorithm independently within AlphaZero.jl, without requiring a neural network. It shows the initialization of a game and the creation of a game specification.

```julia
using AlphaZero

# Create game
game_spec = AlphaZero.Examples.games["tictactoe"]
game = AlphaZero.GI.init(game_spec)
```

--------------------------------

### Simulation Parameters

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/params.md

Documents parameters related to simulation execution.

```APIDOC
## Simulation Parameters

### Description
This section details the parameters used for controlling simulations.

### Method
N/A (Documentation only)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
#### Success Response (200)
N/A

#### Response Example
N/A
```

--------------------------------

### Explore AlphaZero Agent Interactively

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/tutorial/connect_four.md

Launches an interactive command interpreter to investigate the current AlphaZero agent. This allows for real-time analysis and interaction with the trained model.

```julia
julia --project -e 'using AlphaZero; Scripts.explore("connect-four")'
```

--------------------------------

### Mandatory Game Interface: Game Environments

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Defines the core functions required for a game environment in AlphaZero.jl, which holds the current state of the game.

```APIDOC
## Mandatory Interface: Game Environments

### Description
This section details the mandatory functions for managing the dynamic state of a game environment.

### Functions
- **`AbstractGameEnv`**: Abstract type for game environments.
- **`init`**: Initializes a new game environment.
- **`spec`**: Returns the game specification associated with the environment.
- **`set_state!`**: Sets the current state of the environment.
- **`current_state`**: Returns the current state of the environment.
- **`game_terminated`**: Checks if the game has ended.
- **`white_playing`**: Returns true if it's white's turn to play.
- **`actions_mask`**: Returns a mask of valid actions for the current state.
- **`play!`**: Executes an action in the environment and updates the state.
```

--------------------------------

### Training Reports API

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/reports.md

Provides access to different aspects of training reports, categorized by phase (Self-Play, Memory Analysis, Learning) and function (Evaluations, Benchmarks).

```APIDOC
## Training Reports Overview

### Description
This section details the structure of training reports generated by AlphaZero.jl. Reports are organized into distinct phases of the training process, including Self-Play, Memory Analysis, and Learning, as well as dedicated sections for Evaluations and Benchmarks.

### Method
GET

### Endpoint
/reports

### Parameters
This endpoint does not have direct parameters. Data is accessed through specific sub-report types.

### Request Example
```json
{
  "message": "Access specific report types via their respective endpoints or documentation."
}
```

### Response
This is a conceptual overview. Specific data structures are detailed under each report type.

## Report Structure

### Report

**Description:** Represents the overarching training report structure.

**Fields:**
- `initial` (Report.Initial) - Information about the initial training state.
- `iterations` (Array{Report.Iteration}) - A list of reports for each training iteration.
- `perfs` (Report.Perfs) - Performance metrics over time.

### Initial Report

**Description:** Details the initial configuration and state of the training process.

**Fields:** (Specific fields depend on `Report.Initial` definition)

### Iteration Report

**Description:** Contains information for a single training iteration.

**Fields:** (Specific fields depend on `Report.Iteration` definition)

### Performance Report

**Description:** Aggregated performance metrics.

**Fields:** (Specific fields depend on `Report.Perfs` definition)

## Self-Play Phase

### SelfPlay Report

**Description:** Report data specific to the self-play phase of training.

**Fields:** (Specific fields depend on `Report.SelfPlay` definition)

## Memory Analysis Phase

### Memory Report

**Description:** Analysis of memory usage during training.

**Fields:**
- `samples` (Report.Samples) - Information about sampled data.
- `stage_samples` (Report.StageSamples) - Sample data broken down by stage.

### Samples Report

**Description:** Details about the samples collected.

**Fields:** (Specific fields depend on `Report.Samples` definition)

### Stage Samples Report

**Description:** Sample data categorized by different training stages.

**Fields:** (Specific fields depend on `Report.StageSamples` definition)

## Learning Phase

### Learning Report

**Description:** Report data related to the learning phase.

**Fields:**
- `checkpoint` (Report.Checkpoint) - Information about training checkpoints.
- `status` (Report.LearningStatus) - The status of the learning process.
- `loss` (Report.Loss) - Loss metrics during learning.

### Checkpoint Report

**Description:** Details about saved training checkpoints.

**Fields:** (Specific fields depend on `Report.Checkpoint` definition)

### Learning Status Report

**Description:** The current status of the learning phase.

**Fields:** (Specific fields depend on `Report.LearningStatus` definition)

### Loss Report

**Description:** Loss values recorded during the learning phase.

**Fields:** (Specific fields depend on `Report.Loss` definition)

## Evaluations and Benchmarks

### Evaluation Report

**Description:** Results from model evaluations.

**Fields:** (Specific fields depend on `Report.Evaluation` definition)

### Benchmark Report

**Description:** Results from performance benchmarks.

**Fields:** (Specific fields depend on `Report.Benchmark` definition)

```

--------------------------------

### Optional Interface: Other Optional Functions

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Contains other optional functions that can be implemented for advanced features.

```APIDOC
## Optional Interface: Other Optional Functions

### Description
This section lists other optional functions that can be implemented for additional game logic or features.

### Functions
- **`heuristic_value`**: Provides a heuristic estimate of the current state's value.
- **`symmetries`**: Returns symmetries of the current state.
```

--------------------------------

### Mandatory Game Interface: Game Specifications

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Defines the core functions required for a game specification in AlphaZero.jl, which holds static information about the game.

```APIDOC
## Mandatory Interface: Game Specifications

### Description
This section details the mandatory functions for defining a game's static properties.

### Functions
- **`AbstractGameSpec`**: Abstract type for game specifications.
- **`two_players`**: Returns true if the game is for two players.
- **`actions`**: Returns all possible actions for the game.
- **`vectorize_state`**: Converts the game state into a vector representation.
```

--------------------------------

### Derived Functions: Operations on Environments

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Derived functions that operate on game environments, offering utility methods for environment manipulation.

```APIDOC
## Derived Functions: Operations on Environments

### Description
This section details derived functions that provide utility for interacting with game environments.

### Functions
- **`clone`**: Creates a deep copy of the game environment.
- **`available_actions`**: Returns a list of actions that can be taken from the current state.
- **`apply_random_symmetry!`**: Applies a random symmetry to the current state.
```

--------------------------------

### Define and Implement Custom Network Architecture in AlphaZero.jl

Source: https://context7.com/jonathan-laurent/alphazero.jl/llms.txt

This snippet illustrates how to define a custom neural network architecture for game evaluation in AlphaZero.jl using Flux. It includes implementing the required interface methods for game specification, hyperparameter handling, forward pass, device chuyển đổi, and parameter access.

```julia
using AlphaZero
using Flux

# Define custom network type
struct CustomNetwork <: AlphaZero.AbstractNetwork
    model
    game_spec
    hyperparams
end

# Implement required interface
AlphaZero.Network.game_spec(nn::CustomNetwork) = nn.game_spec
AlphaZero.Network.hyperparams(nn::CustomNetwork) = nn.hyperparams

AlphaZero.Network.forward(nn::CustomNetwork, state) = nn.model(state)

AlphaZero.Network.to_gpu(nn::CustomNetwork) = CustomNetwork(
    gpu(nn.model), nn.game_spec, nn.hyperparams)

AlphaZero.Network.to_cpu(nn::CustomNetwork) = CustomNetwork(
    cpu(nn.model), nn.game_spec, nn.hyperparams)

AlphaZero.Network.on_gpu(nn::CustomNetwork) = nn.model isa Flux.GPU

AlphaZero.Network.set_test_mode!(nn::CustomNetwork, mode=true) =
    mode ? Flux.testmode!(nn.model) : Flux.trainmode!(nn.model)

AlphaZero.Network.convert_input(nn::CustomNetwork, input) =
    Float32.(input) |> (on_gpu(nn) ? gpu : cpu)

AlphaZero.Network.convert_output(nn::CustomNetwork, output) =
    output |> cpu

AlphaZero.Network.params(nn::CustomNetwork) = Flux.params(nn.model)

AlphaZero.Network.regularized_params(nn::CustomNetwork) =
    [p for p in Flux.params(nn.model) if length(size(p)) > 1]

Base.copy(nn::CustomNetwork) = CustomNetwork(
    deepcopy(nn.model), nn.game_spec, nn.hyperparams)

# Create custom network instance
struct CustomNetHP
    num_layers::Int
    hidden_size::Int
end

AlphaZero.Network.HyperParams(::Type{CustomNetwork}) = CustomNetHP

function CustomNetwork(gspec::AlphaZero.GI.AbstractGameSpec, hp::CustomNetHP)
    state_size = prod(AlphaZero.GI.state_dim(gspec))
    num_actions = AlphaZero.GI.num_actions(gspec)

    model = Chain(
        Dense(state_size, hp.hidden_size, relu),
        [Dense(hp.hidden_size, hp.hidden_size, relu) for _ in 1:hp.num_layers-1]…,
        Dense(hp.hidden_size, num_actions + 1))

    return CustomNetwork(model, gspec, hp)
end
```

--------------------------------

### Derived Functions: Operations on Specifications

Source: https://github.com/jonathan-laurent/alphazero.jl/blob/master/docs/src/reference/game_interface.md

Derived functions that operate on game specifications, providing utility methods.

```APIDOC
## Derived Functions: Operations on Specifications

### Description
This section covers derived functions that perform operations related to game specifications.

### Functions
- **`state_type`**: Returns the type of the game state.
- **`state_dim`**: Returns the dimension of the state representation.
- **`state_memsize`**: Returns the memory size of the state representation.
- **`action_type`**: Returns the type of the game actions.
- **`num_actions`**: Returns the total number of possible actions.
- **`init(::AbstractGameSpec, state)`**: Initializes a game with a specific starting state.
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.