Iris with NeuralUCB

In this tutorial, we will be training a NeuralUCB agent to solve the Iris dataset, converted into a bandit environment.

To complete the Iris environment, the agent must learn to select the best arm, or action, to take in a given context, or state.

Figure 1: Cumulative regret from training on the Iris dataset	Figure 2: Reward from training on the Iris dataset

NeuralUCB (Neural Contextual Bandits with UCB-based Exploration) utilizes the representational capabilities of deep neural networks and employs a neural network-based random feature mapping to create an upper confidence bound (UCB) for reward, enabling efficient exploration.

For this tutorial, we will use the labelled Iris dataset from the UCI Machine Learning Repository. These datasets can easily be imported and used for training with the Python package ucimlrepo, and to choose from the hundreds of available datasets it is as simple as changing the id parameter used by fetch_uci_repo. We can convert these labelled datasets into a bandit learning environment easily by using the agilerl.wrappers.learning.BanditEnv class.

"""This tutorial shows how to train an NeuralUCB agent on the IRIS dataset.

Authors: Nick (https://github.com/nicku-a)
"""

import matplotlib.pyplot as plt
import numpy as np
import torch
from scipy.ndimage import gaussian_filter1d
from tqdm import trange
from ucimlrepo import fetch_ucirepo

from agilerl.components.replay_buffer import ReplayBuffer
from agilerl.utils.utils import initialPopulation
from agilerl.wrappers.learning import BanditEnv

if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    NET_CONFIG = {
        "arch": "mlp",  # Network architecture
        "hidden_size": [128],  # Actor hidden size
    }

    INIT_HP = {
        "POPULATION_SIZE": 1,  # Population size
        "BATCH_SIZE": 64,  # Batch size
        "LR": 1e-3,  # Learning rate
        "GAMMA": 1.0,  # Scaling factor
        "LAMBDA": 1.0,  # Regularization factor
        "REG": 0.000625,  # Loss regularization factor
        "LEARN_STEP": 1,  # Learning frequency
        # Swap image channels dimension from last to first [H, W, C] -> [C, H, W]
        "CHANNELS_LAST": False,
    }

    # Fetch data  https://archive.ics.uci.edu/
    iris = fetch_ucirepo(id=53)
    features = iris.data.features
    targets = iris.data.targets

    env = BanditEnv(features, targets)  # Create environment
    context_dim = env.context_dim
    action_dim = env.arms

    pop = initialPopulation(
        algo="NeuralUCB",  # Algorithm
        state_dim=context_dim,  # State dimension
        action_dim=action_dim,  # Action dimension
        one_hot=None,  # One-hot encoding
        net_config=NET_CONFIG,  # Network configuration
        INIT_HP=INIT_HP,  # Initial hyperparameters
        population_size=INIT_HP["POPULATION_SIZE"],  # Population size
        device=device,
    )

    field_names = ["context", "reward"]
    memory = ReplayBuffer(
        action_dim=action_dim,  # Number of agent actions
        memory_size=10000,  # Max replay buffer size
        field_names=field_names,  # Field names to store in memory
        device=device,
    )

    max_episodes = 50  # Max training episodes
    max_steps = 50  # Max steps per episode

    evo_epochs = 2  # Evolution frequency
    evo_loop = 1  # Number of evaluation episodes

    print("Training...")

    regret = [[0] for _ in pop]
    score = [[0] for _ in pop]
    total_steps = 0

    # TRAINING LOOP
    for idx_epi in trange(max_episodes):
        for i, agent in enumerate(pop):  # Loop through population
            losses = []
            context = env.reset()  # Reset environment at start of episode
            for idx_step in range(max_steps):
                # Get next action from agent
                action = agent.getAction(context)
                next_context, reward = env.step(action)  # Act in environment

                # Save experience to replay buffer
                memory.save2memory(context[action], reward)

                # Learn according to learning frequency
                if (
                    memory.counter % agent.learn_step == 0
                    and len(memory) >= agent.batch_size
                ):
                    for _ in range(2):
                        experiences = memory.sample(
                            agent.batch_size
                        )  # Sample replay buffer
                        # Learn according to agent's RL algorithm
                        loss = agent.learn(experiences)
                        losses.append(loss)

                context = next_context
                score[i].append(reward)
                regret[i].append(regret[i][-1] + 1 - reward)

            total_steps += max_steps

        # Now evaluate population
        if (idx_epi + 1) % evo_epochs == 0:
            # Evaluate population
            fitnesses = [
                agent.test(
                    env,
                    swap_channels=INIT_HP["CHANNELS_LAST"],
                    max_steps=max_steps,
                    loop=evo_loop,
                )
                for agent in pop
            ]

            print(f"Episode {idx_epi+1}/{max_episodes}")
            print(f"Regret: {[regret[i][-1] for i in range(len(pop))]}")

    # Plot the results
    plt.figure()
    for i, agent_regret in enumerate(regret):
        plt.plot(
            np.linspace(0, total_steps, len(agent_regret)),
            agent_regret,
            label=f"NeuralTS: Agent {i}",
        )
    plt.xlabel("Training Step")
    plt.ylabel("Regret")
    plt.legend()
    plt.savefig("NeuralUCB-IRIS-regret.png")

    plt.figure()
    for i, agent_score in enumerate(score):
        smoothed_score = gaussian_filter1d(agent_score, sigma=80)
        plt.plot(
            np.linspace(0, total_steps, len(smoothed_score)),
            smoothed_score,
            label=f"NeuralTS: Agent {i}",
        )
    plt.xlabel("Training Step")
    plt.ylabel("Reward")
    plt.legend()
    plt.savefig("NeuralUCB-IRIS-reward.png")