Belief-state planning algorithm for POMDPs using learned approximations; integrated into the POMDPs.jl ecosystem.
To install the BetaZero solver, run:
using Pkg
pkg"add https://github.com/sisl/BetaZero.jl"(Optional) To install the supporting example POMDP models (e.g., LightDark and MinEx), the RemoteJobs package, and the ParticleBeliefs wrapper, run:
using BetaZero
install_extras()The following code sets up the necessary interface functions BetaZero.input_representation and the optional BetaZero.accuracy for the LightDark POMDP problem and solves it using BetaZero.
using BetaZero
using LightDark
pomdp = LightDarkPOMDP()
up = BootstrapFilter(pomdp, 500)
function BetaZero.input_representation(b::ParticleCollection{LightDarkState})
# Function to get belief representation as input to neural network.
μ, σ = mean_and_std(s.y for s in particles(b))
return Float32[μ, σ]
end
function BetaZero.accuracy(pomdp::LightDarkPOMDP, b0, s0, states, actions, returns)
# Function to determine accuracy of agent's final decision.
return returns[end] == pomdp.correct_r
end
solver = BetaZeroSolver(pomdp=pomdp,
updater=up,
params=BetaZeroParameters(
n_iterations=50,
n_data_gen=50,
),
nn_params=BetaZeroNetworkParameters(
pomdp, up;
training_epochs=50,
n_samples=100_000,
batchsize=1024,
learning_rate=1e-4,
λ_regularization=1e-5,
use_dropout=true,
p_dropout=0.2,
),
verbose=true,
collect_metrics=true,
plot_incremental_data_gen=true)
policy = solve(solver, pomdp)
save_policy(policy, "policy.bson")
save_solver(solver, "solver.bson")This example is also located at: scripts/readme_example.jl
Using the Distributed package, you can easily launch workers to run the MCTS data collection in parallel.
using Distributed
addprocs([("user@hostname1", 25), ("user@hostname2", 25)], tunnel=true) # launch 50 processes across two separate hosts
@everywhere begin
using BetaZero
using LightDark
pomdp = LightDarkPOMDP()
up = BootstrapFilter(pomdp, 500)
function BetaZero.input_representation(b::ParticleCollection{LightDarkState})
# Function to get belief representation as input to neural network.
μ, σ = mean_and_std(s.y for s in particles(b))
return Float32[μ, σ]
end
function BetaZero.accuracy(pomdp::LightDarkPOMDP, b0, s0, states, actions, returns)
# Function to determine accuracy of agent's final decision.
return returns[end] == pomdp.correct_r
end
end
solver = BetaZeroSolver(pomdp=pomdp,
updater=up,
params=BetaZeroParameters(
n_iterations=50,
n_data_gen=500, # Note increased to 500 when running in parallel.
),
nn_params=BetaZeroNetworkParameters(
pomdp, up;
training_epochs=50,
n_samples=100_000,
batchsize=1024,
learning_rate=1e-4,
λ_regularization=1e-5,
use_dropout=true,
p_dropout=0.2,
),
verbose=true,
collect_metrics=true,
plot_incremental_data_gen=true)
policy = solve(solver, pomdp)
save_policy(policy, "policy.bson")
save_solver(solver, "solver.bson")This example is also located at: scripts/readme_example_parallel.jl
See the following files for more examples:
- LightDark POMDP
- Training
scripts/train_lightdark.jl - Solver
scripts/solver_lightdark.jl - Representation
scripts/representation_lightdark.jl
- Training
- RockSample POMDP
- Training
scripts/train_rocksample.jl - Solver
scripts/solver_rocksample.jl - Representation
scripts/representation_rocksample.jl
- Training
- Mineral Exploration POMDP
- Training
scripts/train_minex.jl - Solver
scripts/solver_minex.jl - Representation
scripts/representation_minex.jl
- Training
The parameters, their descriptions, and their defaults are defined in src/parameters.jl
"""
Parameters for the BetaZero algorithm.
"""
@with_kw mutable struct BetaZeroParameters
n_iterations::Int = 10 # BetaZero policy iterations (primary outer loop).
n_data_gen::Int = 100 # Number of episodes to run for training/validation data generation.
n_evaluate::Int = 0 # Number of episodes to run for surrogate evaluation and comparison (when `n_evaluate == 0`, skip the evaluation step)
n_holdout::Int = 0 # Number of episodes to run for a holdout test set (on a fixed, non-training or evaluation set).
n_buffer::Int = 1 # Number of iterations to keep data for surrogate training (NOTE: each simulation has multiple time steps of data, not counted in this number. This number corresponds to the number of iterations, i.e., set to 2 if you want to keep data from the previous 2 policy iterations.)
max_steps::Int = 100 # Maximum number of steps for each simulation.
λ_ucb::Real = 0.0 # Upper confidence bound parameter for network evaluation/comparison: μ + λσ
use_nn::Bool = true # Use neural network as the surrogate model
use_completed_policy_gumbel::Bool = false # When using the Gumbel solver, use the completed policy estimate as the policy data
use_raw_policy_network::Bool = false # Generate data only from the raw policy network
use_raw_value_network::Bool = false # Generate data only from the raw value network (given a `n_obs` below)
raw_value_network_n_obs::Int = 1 # When using the raw value network via `use_raw_value_network`, specify number of observations per action to expand on
skip_missing_reward_signal::Bool = false # When running MCTS episodes, filter out trajectories that had no reward signal (i.e., zero reward everywhere)
train_missing_on_predicted::Bool = false # Use predicted value in place of missing reward signal episodes
eval_on_accuracy::Bool = false # If evaluating (i.e., `n_evaluate > 0`), then base comparison on accuracy of the two networks
bootstrap_q::Bool = false # Bootstrap the `init_Q` using the value network when a new (b,a) node is added during MCTS.
end
"""
Parameters for neural network surrogate model.
"""
@with_kw mutable struct BetaZeroNetworkParameters
action_size::Int # [REQUIRED] Number of actions in the action space
input_size = (30,30,5) # Input belief size
training_epochs::Int = 1000 # Number of network training updates
n_samples::Int = 10_000 # Number of samples (i.e., simulated POMDP time steps from data collection) to use during training + validation
normalize_input::Bool = true # Normalize input data to standard normal (0 mean)
normalize_output::Bool = true # Normalize output (target) data to standard normal (0 mean)
training_split::Float64 = 0.8 # Training / validation split (Default: 80/20)
sample_more_than_collected::Bool = true # Sample more data (with replacement) than is in the buffer
batchsize::Int = 512 # Batch size
learning_rate::Float64 = 1e-3 # Learning rate for ADAM optimizer during training
λ_regularization::Float64 = 1e-5 # Parameter for L2-norm regularization
optimizer = Adam # Training optimizer (e.g., Adam, Descent, Nesterov)
loss_func::Function = Flux.Losses.mse # MAE works well for problems with large returns around zero, and spread out otherwise.
activation::Function = relu # Activation function
layer_size::Int = 64 # Number of connections in fully connected layers (for CNN, refers to fully connected "head" layers)
use_cnn::Bool = false # Use convolutional neural network
use_deepmind_arch::Bool = false # Use simplified non-resnet architecture from AlphaZero
cnn_params::NamedTuple = (filter=(5,5), num_filters=[64, 128], num_dense=[256, 256])
use_dropout::Bool = false # Indicate the use of dropout layers
p_dropout::Float64 = 0.2 # Probability of dropout
use_batchnorm::Bool = false # Indicate the use of batch normalization layers
batchnorm_momentum = 0.1f0 # Momentum parameter for batch normalization
use_dirichlet_exploration::Bool = false # Apply Dirichlet noise to policy vector for exploration
α_dirichlet::Float64 = 0.03 # Alpha parameter of the Dirichlet action noise distribution
ϵ_dirichlet::Float64 = 0.25 # Weighting parameter for applying Dirichlet action noise
use_prioritized_action_selection::Bool = true # When performing action branching, select new actions from the policy network
zero_out_tried_actions::Bool = false # When selecting a next action to widen on, zero out the probabilities for already tried actions.
next_action_return_argmax::Bool = false # Instead of sampling, return the argmax action during action widening
use_epsilon_greedy::Bool = false # Use epsilon-greedy exploration during action widening
ϵ_greedy::Float64 = 0.0 # Epsilon parameter to select random action during widening with probability ϵ_greedy
value_loss_weight::Float32 = 0.5f0 # Weight applied to the value component of the loss function
use_kl_loss::Bool = false # Use KL-divergence as classification (policy) loss (for Gumbel solver)
incremental_save::Bool = false # Incrementally save off policy every iteration (TODO: fix undefined reference error)
policy_filename::String = "betazero_policy.bson" # Filename when incrementally saving off poliy
device = gpu # Indicate what device to train on (`gpu` or `cpu`)
use_checkpoint::Bool = true # Save networks along the way to use based on minimum validation loss
checkpoint_frequency::Int = 1 # How often do we evaluate and save a checkpoint?
checkpoint_validation_loss::Bool = true # Checkpoint based on minimum validation loss (`false` = checkpointing on training loss)
stop_short::Bool = true # Cut the training off prematurely
stop_short_threshold::Int = 1000 # Cut training off if the validation loss has not improved for the set number of epochs
verbose_update_frequency::Int = training_epochs # Frequency of printed training output
verbose_plot_frequency::Number = Inf # Frequency of plotted training/validation output
display_plots::Bool = false # Display training and validation plots after training
save_plots::Bool = false # Save training and validation plots after training
plot_curve_filename::String = "training_curve.png" # Filename for the training/validation loss curve
plot_value_distribution_filename::String = "value_distribution.png" # Filename for the distribution of values (model vs. data)
plot_training_bias_filename::String = "training_data.png" # Filename the bias plots for training model vs. data
plot_validation_bias_filename::String = "validation_data.png" # Filename the bias plots for validation model vs. data
endThe default MCTS PUCT parameters are defined in src/BetaZero.jl and submodules/MCTS/src/puct_types.jl.
solver = BetaZeroSolver(...)
solver.mcts_solver = PUCTSolver(
n_iterations = 100,
exploration_constant = 1.0,
k_action = 2.0,
alpha_action = 0.25,
k_state = 2.0,
alpha_state = 0.1,
counts_in_info = true, # Note, required for policy vector.
final_criterion = MaxZQN(zq=1, zn=1),
)Final criteria parameters are defined in submodules/MCTS/src/criteria.jl.
# To sample from tree policy when selecting root node actions (ensures exploration), use:
solver.mcts_solver.final_criterion = SampleZQN(τ=1, zq=1, zn=1)
# To get the maximizing action, use:
solver.mcts_solver.final_criterion = MaxZQN(zq=1, zn=1)
# (which is equivalent to):
solver.mcts_solver.final_criterion = SampleZQN(τ=0, zq=1, zn=1).
├── media # Image files.
├── scripts # Example scripts for training POMDPs, visualizations, and baselines.
├── src # Core Julia package files with BetaZero implementation and supporting code.
├── submodules # Submodules used by the scripts.
│ └── CryingBaby # POMDP model for the crying baby problem.
│ └── LightDark # POMDP model for the light dark problem.
│ └── MCTS # MCTS.jl [1] fork with PUCT and BetaZero root action criteria.
│ └── MinEx # POMDP model for the mineral exploration problem.
│ └── ParticleBeliefs # Lightweight wrapper for a particle filter that stores actions and observations.
│ └── RemoteJobs # Lightweight package for launching remote workers and syncing code.
│ └── RobotLocalization # POMDP model for the robot localization problem (adapted from RoombaPOMDPs.jl [2]).
│ └── Tiger # POMDP model for the tiger problem.
└── tex # LaTeX files for the TikZ mineral exploration policy maps.
[1] https://github.com/JuliaPOMDP/MCTS.jl
[2] https://github.com/sisl/RoombaPOMDPs.jl
Under review.