mirror of
https://github.com/vale981/ray
synced 2025-03-08 11:31:40 -05:00
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
""" Example of using LinUCB on a recommendation environment with parametric
|
|
actions. """
|
|
|
|
import os
|
|
import time
|
|
|
|
from matplotlib import pyplot as plt
|
|
import pandas as pd
|
|
|
|
from ray import tune
|
|
from ray.rllib.contrib.bandits.agents import LinUCBTrainer
|
|
from ray.rllib.contrib.bandits.agents.lin_ucb import UCB_CONFIG
|
|
from ray.rllib.contrib.bandits.envs import ParametricItemRecoEnv
|
|
|
|
if __name__ == "__main__":
|
|
|
|
# Temp fix to avoid OMP conflict
|
|
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
|
|
|
|
UCB_CONFIG["env"] = ParametricItemRecoEnv
|
|
|
|
# Actual training_iterations will be 20 * timesteps_per_iteration
|
|
# (100 by default) = 2,000
|
|
training_iterations = 20
|
|
|
|
print("Running training for %s time steps" % training_iterations)
|
|
|
|
start_time = time.time()
|
|
analysis = tune.run(
|
|
LinUCBTrainer,
|
|
config=UCB_CONFIG,
|
|
stop={"training_iteration": training_iterations},
|
|
num_samples=5,
|
|
checkpoint_at_end=False)
|
|
|
|
print("The trials took", time.time() - start_time, "seconds\n")
|
|
|
|
# Analyze cumulative regrets of the trials
|
|
frame = pd.DataFrame()
|
|
for key, df in analysis.trial_dataframes.items():
|
|
frame = frame.append(df, ignore_index=True)
|
|
x = frame.groupby("num_steps_trained")[
|
|
"learner/cumulative_regret"].aggregate(["mean", "max", "min", "std"])
|
|
|
|
plt.plot(x["mean"])
|
|
plt.fill_between(
|
|
x.index,
|
|
x["mean"] - x["std"],
|
|
x["mean"] + x["std"],
|
|
color="b",
|
|
alpha=0.2)
|
|
plt.title("Cumulative Regret")
|
|
plt.xlabel("Training steps")
|
|
plt.show()
|