def averaged_runs(µ, σ, ε, selector, provider_factory, initializer,
dont_average_reward):
exs = [
Experiment(µ, σ, ε,
ctx().num_arms, selector, provider_factory, initializer,
dont_average_reward) for _ in range(ctx().num_runs)
]
_ = [ex.run(ctx().num_pulls_per_run) for ex in tqdm(exs)]
rewards = np.average(np.stack([ex.rewards for ex in exs], axis=1), axis=1)
percent_optimal_actions = 100 * np.average(
np.stack([ex.optimality for ex in exs], axis=1), axis=1)
return rewards, percent_optimal_actions
def run_experiment():
results = [
averaged_runs(ctx().ms[i],
ctx().σs[i],
ctx().εs[i],
ctx().selectors[i](ctx().εs[i], ctx().num_arms),
ctx().providers[i],
ctx().initializers[i](),
ctx().dont_average_reward) for i in range(len(ctx().εs))
]
rewards = [r[0] for r in results]
percent_optimals = [r[1] for r in results]
return rewards, percent_optimals
from munch import munchify
%matplotlib inline
#───────────────────────────────────────────────────────────────────────
# Experiment Start
#───────────────────────────────────────────────────────────────────────
use_context(1)
before = t.perf_counter()
rewards, percent_optimals = run_experiment()
print("time (seconds): {0}".format(t.perf_counter() - before))
#───────────────────────────────────────────────────────────────────────
# Plotting
#───────────────────────────────────────────────────────────────────────
labels = ["ε={0}{1}".format(ε, (" (greedy)" if ε == 0 else "")) for ε in ctx().εs]
pp.figure(figsize=(10,4))
[pp.plot(rewards[i], label=labels[i]) for i in range(len(ctx().εs))]
pp.legend(bbox_to_anchor=(1.2, 0.5))
pp.xlabel("Steps")
pp.ylabel("Average Reward")
pp.title("Average ε-greedy Rewards over " + str(ctx().num_runs) + " Runs")
pp.show()
pp.figure(figsize=(10,4))
[pp.plot(percent_optimals[i], label=labels[i]) for i in range(len(ctx().εs))]
pp.legend(bbox_to_anchor=(1.2, 0.5))
pp.xlabel("Steps")
pp.ylabel("% Optimal Action")
pp.title("% times optical action selected averaged over " + str(ctx().num_runs) + " Runs")
pp.show()
from BanditMachine import BanditMachine
from Experiment import run_experiment
from Insanity.Initializers import OptimisticInitializer, ZeroInitializer
from ActionValuesProvider import ActionValuesProvider
from Context import ctx, use_context
from munch import munchify
%matplotlib inline
#───────────────────────────────────────────────────────────────────────
# Experiment Start
#───────────────────────────────────────────────────────────────────────
use_context(2)
before = t.perf_counter()
rewards, percent_optimals = run_experiment()
print("time (seconds): {0}".format(t.perf_counter() - before))
#───────────────────────────────────────────────────────────────────────
# Plotting
#───────────────────────────────────────────────────────────────────────
labels = ["ε={0}{1}".format(ε, (" (greedy, optimistic, Q₁=5), " if ε == 0 else " realistic, Q₁=0")) for ε in ctx().εs]
pp.figure(figsize=(10,4))
[pp.plot(percent_optimals[i], label=labels[i]) for i in range(len(ctx().εs))]
pp.legend(bbox_to_anchor=(1.2, 0.5))
pp.xlabel("Steps")
pp.ylabel("% Optimal Action")
pp.title("% times optical action selected averaged over " + str(ctx().num_runs) + " Runs")
pp.show()
import numpy as np
from tqdm import tqdm
from BanditMachine import BanditMachine
from Experiment import run_experiment
from Insanity.Initializers import OptimisticInitializer, ZeroInitializer
from ActionValuesProvider import ActionValuesProvider
from Context import ctx, use_context
from munch import munchify
%matplotlib inline
#───────────────────────────────────────────────────────────────────────
# Experiment Start
#───────────────────────────────────────────────────────────────────────
use_context(4)
before = t.perf_counter()
rewards, percent_optimals = run_experiment()
print("time (seconds): {0}".format(t.perf_counter() - before))
#───────────────────────────────────────────────────────────────────────
# Plotting
#───────────────────────────────────────────────────────────────────────
labels = ["α={0}{1}".format(α, " (no baseline)" if i > 1 else "") for i,α in enumerate([0.1, 0.4, 0.1, 0.4])]
pp.figure(figsize=(10,4))
[pp.plot(percent_optimals[i], label=labels[i]) for i in range(len(ctx().εs))]
pp.legend(bbox_to_anchor=(1.2, 0.5))
pp.xlabel("Steps")
pp.ylabel("% Optimal Action")
pp.title("Gradient Bandit (averaged over " + str(ctx().num_runs) + " Runs)")
pp.show()