Illustration of plotting tools on Bandits

This script shows how to define a bandit environment and an UCB Index-based algorithm.

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/home/runner/work/rlberry/rlberry/main/rlberry/manager/plotting.py:618: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()
/home/runner/work/rlberry/rlberry/main/rlberry/manager/plotting.py:618: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()
/home/runner/work/rlberry/rlberry/main/rlberry/manager/plotting.py:618: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()
/home/runner/work/rlberry/rlberry/main/rlberry/manager/plotting.py:467: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()
/home/runner/work/rlberry/rlberry/main/rlberry/manager/plotting.py:467: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()
/home/runner/work/rlberry/rlberry/main/rlberry/manager/plotting.py:467: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()

import numpy as np
from rlberry_research.envs.bandits import NormalBandit
from rlberry_research.agents.bandits import (
    IndexAgent,
    makeSubgaussianUCBIndex,
    makeSubgaussianMOSSIndex,
)
from rlberry.manager import ExperimentManager, plot_writer_data
import matplotlib.pyplot as plt


# Parameters of the problem
means = np.array([0, 0.9, 1])  # means of the arms
T = 3000  # Horizon
M = 20  # number of MC simu


# Agents definition
class UCBAgent(IndexAgent):
    """UCB agent for sigma-subgaussian bandits"""

    name = "UCB Agent"

    def __init__(self, env, sigma=1, **kwargs):
        index, _ = makeSubgaussianUCBIndex(sigma)
        IndexAgent.__init__(self, env, index, writer_extra="action", **kwargs)


class MOSSAgent(IndexAgent):
    """Moss agent for sigma-subgaussian bandits"""

    name = "Moss Agent"

    def __init__(self, env, sigma=1, **kwargs):
        index, _ = makeSubgaussianMOSSIndex(T, len(means), sigma=sigma)
        IndexAgent.__init__(self, env, index, writer_extra="action", **kwargs)


# Construction of the experiment

env_ctor = NormalBandit
env_kwargs = {"means": means, "stds": 2 * np.ones(len(means))}

agent1 = ExperimentManager(
    UCBAgent,
    (env_ctor, env_kwargs),
    fit_budget=T,
    init_kwargs={"sigma": 2},
    n_fit=M,
    parallelization="process",
    mp_context="fork",
)

agent2 = ExperimentManager(
    MOSSAgent,
    (env_ctor, env_kwargs),
    fit_budget=T,
    init_kwargs={"sigma": 2},
    n_fit=M,
    parallelization="process",
    mp_context="fork",
)
# these parameters should give parallel computing even in notebooks


# Agent training

agent1.fit()
agent2.fit()


# Compute and plot (pseudo-)regret
def compute_pseudo_regret(actions):
    return np.cumsum(np.max(means) - means[actions.astype(int)])


fig, axes = plt.subplots(2, 2, figsize=(9, 5))
axes = axes.ravel()
fig.tight_layout(pad=5.0)  # give some space for titles

plt.suptitle("Cumulative Pseudo-Regret")

for i, error in enumerate(["raw_curves", "ci", "pi"]):
    output = plot_writer_data(
        [agent1, agent2],
        tag="action",
        preprocess_func=compute_pseudo_regret,
        title=error,
        smooth=False,
        error_representation=error,
        ax=axes[i],
        show=False,
    )


fig, axes = plt.subplots(2, 2, figsize=(9, 5))
axes = axes.ravel()
fig.tight_layout(pad=5.0)  # give some space for titles

plt.suptitle("Cumulative Pseudo-Regret --- smoothed")

for i, error in enumerate(["cb", "raw_curves", "ci", "pi"]):
    output = plot_writer_data(
        [agent1, agent2],
        tag="action",
        preprocess_func=compute_pseudo_regret,
        title=error,
        smooth=True,
        error_representation=error,
        ax=axes[i],
        show=False,
    )


plt.show()