def _multiple_env(n_arms, n_step, n_test, prob_of_change, type_change):
np.random.seed()
# Build Agents
ts = mab.BernoulliThompsonSampling(n_arms)
discounted_ts = mab.DiscountedBernoulliTS(n_arms, gamma=0.98)
sw_ts = mab.BernoulliSlidingWindowTS(n_arms, n=100)
max_dsw_ts = mab.MaxDSWTS(n_arms, gamma=0.99, n=25)
min_dsw_ts = mab.MinDSWTS(n_arms, gamma=0.95, n=100)
mean_dsw_ts = mab.MeanDSWTS(n_arms, gamma=0.95, n=25)
agents = [ts, discounted_ts, sw_ts, max_dsw_ts, min_dsw_ts, mean_dsw_ts]
# Build Env with replay
replay_env = mab.BernoulliReplayBandit(n_step=n_step,
n_arms=n_arms,
prob_of_change=prob_of_change,
fixed_action_prob=0.0,
type_change=type_change)
# Build session
replay_session = mab.Session(replay_env, agents)
# Run session
replay_session.run(n_step=n_step, n_test=n_test, use_replay=True)
results = {
agent: replay_session.get_reward_sum(agent) / n_step
for agent in agents
}
results.update(
{"Oracle": replay_session.get_reward_sum("Oracle") / n_step})
return results
def _run(self, fake) -> Dict:
########## BUILD AGENTS ###########
max_dsw_ts = mab.MaxDSWTS(n_arms=self._n_arms,
gamma=0.99999,
n=2000,
store_estimates=False)
min_dsw_ts = mab.MinDSWTS(n_arms=self._n_arms,
gamma=0.99999,
n=2000,
store_estimates=False)
mean_dsw_ts = mab.MeanDSWTS(n_arms=self._n_arms,
gamma=0.99999,
n=2000,
store_estimates=False)
ts = mab.BernoulliThompsonSampling(n_arms=self._n_arms,
store_estimates=False)
sw_ts = mab.BernoulliSlidingWindowTS(n_arms=self._n_arms,
n=240000,
store_estimates=False)
d_ts = mab.DiscountedBernoulliTS(n_arms=self._n_arms,
gamma=0.99999,
store_estimates=False)
agent_list = [
max_dsw_ts, min_dsw_ts, mean_dsw_ts, ts, sw_ts, d_ts, "random"
]
np.random.seed()
c = self._compression
reward_trace = {agent: [0] for agent in agent_list}
reward_sum = {agent: 0 for agent in agent_list}
for step in trange(self._n_step):
for agent in agent_list:
if agent == "random": action = random.randint(6)
else: action = agent.select_action()
cluster, click = self.select_cluster(step)
if (cluster == action) and (click == 1): reward = 1
else: reward = 0
# Update statistics
reward_sum[agent] += reward
if step % c == 0:
reward_trace[agent].append(reward_trace[agent][-1] +
reward / c)
else:
reward_trace[agent][-1] += reward / c
#Update agent estimates
if agent != "random":
agent.update_estimates(action, reward)
for agent in agent_list:
reward_sum[agent] /= self._n_step
return (reward_trace, reward_sum)
def test_bernoulli_algorithms():
n_arms = 4
env = mab.BernoulliBandit(n_arms)
greedy_agent = mab.BernoulliGreedy(n_arms)
ts_agent = mab.BernoulliThompsonSampling(n_arms)
ucb_agent = mab.BernoulliUCB(n_arms, c=1)
Discounted_ts_agent = mab.DiscountedBernoulliTS(n_arms, gamma=0.99)
session = mab.Session(
env, [greedy_agent, ts_agent, ucb_agent, Discounted_ts_agent])
session.run(3000)
'''
def custom_environments(n_arms, n_test, test_number: int) -> Tuple:
# Build environment
replay = {}
if test_number == 1:
# TEST 1
replay = {
'probabilities': [0.9, 0.7, 0.1, 0.3],
250: [(0, 0.0)],
500: [(1, 0.0)]
}
elif test_number == 2:
# TEST 2
replay = {
'probabilities': [0.0, 0.0, 0.1, 0.3],
250: [(0, 0.7)],
500: [(1, 0.9)]
}
elif test_number == 3:
# TEST 3
replay = {'probabilities': [0.2, 0.3, 0.4, 0.5]}
replay_env = mab.BernoulliReplayBandit(replay=replay)
# Build Agents
ts = mab.BernoulliThompsonSampling(n_arms)
discounted_ts = mab.DiscountedBernoulliTS(n_arms, gamma=0.99)
sw_ts = mab.BernoulliSlidingWindowTS(n_arms, n=100)
max_dsw_ts = mab.MaxDSWTS(n_arms, gamma=0.99, n=50)
min_dsw_ts = mab.MinDSWTS(n_arms, gamma=0.95, n=75)
mean_dsw_ts = mab.MeanDSWTS(n_arms, gamma=0.99, n=50)
agents = [ts, discounted_ts, sw_ts, max_dsw_ts, min_dsw_ts, mean_dsw_ts]
# Build session
replay_session = mab.Session(replay_env, agents)
# Run session
replay_session.run(n_step=1000, n_test=n_test, use_replay=True)
return pd.DataFrame.from_dict(
replay_session._regrets), pd.DataFrame.from_dict(
replay_session._real_reward_trace)
def _best_agents(self, n_arms) -> List:
return [
mab.MaxDSWTS(n_arms=n_arms,
gamma=0.9999,
n=800,
store_estimates=False),
mab.MinDSWTS(n_arms=n_arms,
gamma=0.99,
n=800,
store_estimates=False),
mab.MeanDSWTS(n_arms=n_arms,
gamma=0.9999,
n=800,
store_estimates=False),
mab.BernoulliThompsonSampling(n_arms=n_arms,
store_estimates=False),
mab.BernoulliSlidingWindowTS(n_arms=n_arms,
n=12800,
store_estimates=False),
mab.DiscountedBernoulliTS(n_arms=n_arms,
gamma=0.9999,
store_estimates=False),
mab.RandomAlgo(n_arms=n_arms)
]
def _run_mod(self, fake) -> Dict:
########## BUILD AGENTS ###########
max_dsw_ts = mab.MaxDSWTS(n_arms=self._n_arms,
gamma=0.99999,
n=2000,
store_estimates=False)
min_dsw_ts = mab.MinDSWTS(n_arms=self._n_arms,
gamma=0.99999,
n=2000,
store_estimates=False)
mean_dsw_ts = mab.MeanDSWTS(n_arms=self._n_arms,
gamma=0.99999,
n=2000,
store_estimates=False)
ts = mab.BernoulliThompsonSampling(n_arms=self._n_arms,
store_estimates=False)
sw_ts = mab.BernoulliSlidingWindowTS(n_arms=self._n_arms,
n=240000,
store_estimates=False)
d_ts = mab.DiscountedBernoulliTS(n_arms=self._n_arms,
gamma=0.99999,
store_estimates=False)
agent_list = [
max_dsw_ts, min_dsw_ts, mean_dsw_ts, ts, sw_ts, d_ts, "random"
]
np.random.seed()
reward_trace = {agent: [0] for agent in agent_list}
reward_sum = {agent: 0 for agent in agent_list}
effective_steps = {agent: 0 for agent in agent_list}
for step in trange(self._n_step):
for agent in agent_list:
# Check if the agent is already reach the "utils iterations"
if effective_steps[agent] < self._termination_step:
if agent == "random": action = random.randint(6)
else: action = agent.select_action()
cluster, click = self.select_cluster(step)
reward = 0
if cluster == action:
effective_steps[agent] += 1
if click == 1: reward = 1
else: reward = 0
# Update statistics
reward_sum[agent] += reward
reward_trace[agent].append(reward_trace[agent][-1] +
reward)
#Update agent estimates
if agent != "random":
agent.update_estimates(action, reward)
else:
print(agent, step)
agent_list.remove(agent)
for key in reward_sum:
reward_sum[key] /= self._termination_step
return (reward_trace, reward_sum)