"""Python Script Template."""
import pandas as pd
import matplotlib.pyplot as plt
from exps.plotting import plot_df_key, set_figure_params
from exps.environments.utilities import get_color, get_linestyle
df = pd.read_pickle("two_state_deterministic_results.pkl")
set_figure_params(serif=True)
fig, axes = plt.subplots(ncols=1, nrows=1)
plot_df_key(
df=df,
axes=axes,
key="value_evaluation",
get_color=get_color,
get_linestyle=get_linestyle,
)
plt.xlabel("Episode")
plt.ylabel("Reward")
plt.title(f"Two-State-Deterministic")
# plt.legend(loc="best", frameon=False)
plt.legend(bbox_to_anchor=(0.52, 0.3), loc="lower left", frameon=False, ncol=2)
plt.savefig("two_state_deterministic_results.pdf", bbox_inches="tight")
get_color,
get_linestyle,
)
import matplotlib.pyplot as plt
import seaborn as sns
palette = sns.color_palette(n_colors=10)
args = parse_arguments()
df = pd.read_pickle("algorithm_results.pkl")
df = df[df.name != "Mini-Batch-REPS"]
df = df[df.name != "Biased-REPS"]
df = df[df.name != "SaddleQREPS"]
set_figure_params(serif=True, fontsize=12)
fig, axes = plt.subplots(ncols=1, nrows=1)
fig.set_size_inches(6.75 / 2, 2.0)
plot_df_key(
df=df,
axes=axes,
key="value_evaluation",
get_color=get_color,
get_linestyle=get_linestyle,
)
plt.xlabel("Episode")
plt.ylabel("Average Reward")
plt.title(r"Min-Max Optimization")