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import os
import math
import time
import gymnasium as gym
import random
import utils
import keras
import numpy as np
from collections import deque
from matplotlib import pyplot as plt
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
RandomForestClassifier,
ExtraTreesClassifier,
AdaBoostClassifier,
)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.exceptions import NotFittedError
from sklearn.ensemble import GradientBoostingClassifier
from tqdm import trange
class ReplayBuffer:
"""
Thank you: https://github.com/BY571/
"""
def __init__(self, max_size):
self.max_size = max_size
self.buffer = []
def add_sample(self, states, actions, rewards):
episode = {
"states": states,
"actions": actions,
"rewards": rewards,
"summed_rewards": sum(rewards),
}
self.buffer.append(episode)
def sort(self):
# sort buffer
self.buffer = sorted(
self.buffer, key=lambda i: i["summed_rewards"], reverse=True
)
# keep the max buffer size
self.buffer = self.buffer[: self.max_size]
def get_random_samples(self, batch_size):
self.sort()
idxs = np.random.randint(0, len(self.buffer), batch_size)
batch = [self.buffer[idx] for idx in idxs]
return batch
def get_n_best(self, n):
self.sort()
return self.buffer[:n]
def __len__(self):
return len(self.buffer)
class UpsideDownAgent:
def __init__(self, environment, approximator):
print(environment)
self.environment = gym.make(environment)
self.approximator = approximator
self.state_size = self.environment.observation_space.shape[0]
self.action_size = self.environment.action_space.n
self.warm_up_episodes = 50
self.render = False
self.memory = ReplayBuffer(700)
self.last_few = 75
self.batch_size = 32
self.command_size = 2 # desired return + desired horizon
self.desired_return = 1
self.desired_horizon = 1
self.horizon_scale = 0.02
self.return_scale = 0.02
self.testing_state = 0
if approximator == "neural_network":
self.behaviour_function = utils.get_functional_behaviour_function(
self.state_size, self.command_size, self.action_size
)
elif approximator == "forest":
self.behaviour_function = RandomForestClassifier(200)
elif approximator == "extra-trees":
self.behaviour_function = ExtraTreesClassifier()
elif approximator == "knn":
self.behaviour_function = KNeighborsClassifier()
elif approximator == "adaboost":
self.behaviour_function = AdaBoostClassifier()
self.testing_rewards = []
self.warm_up_buffer()
def warm_up_buffer(self):
for i in range(self.warm_up_episodes):
# Gymnasium returns (state,info_dict)
state, _ = self.environment.reset()
states = []
rewards = []
actions = []
done = False
desired_return = 1
desired_horizon = 1
while not done:
state = np.reshape(state, [1, self.state_size])
states.append(state)
observation = state
command = np.asarray(
[
desired_return * self.return_scale,
desired_horizon * self.horizon_scale,
]
)
command = np.reshape(command, [1, len(command)])
action = self.get_action(observation, command)
actions.append(action)
# Gymnasium returns (s,r,tr,te,info)
next_state, reward, tru, ter, info = self.environment.step(action)
done = tru or ter
next_state = np.reshape(next_state, [1, self.state_size])
rewards.append(reward)
state = next_state
desired_return -= reward # Line 8 Algorithm 2
desired_horizon -= 1 # Line 9 Algorithm 2
desired_horizon = np.maximum(desired_horizon, 1)
self.memory.add_sample(states, actions, rewards)
def get_action(self, observation, command):
"""
We will sample from the action distribution modeled by the Behavior Function
"""
if self.approximator == "neural_network":
action_probs = self.behaviour_function.predict([observation, command])
action = np.random.choice(np.arange(0, self.action_size), p=action_probs[0])
return action
elif self.approximator in ["forest", "extra-trees", "knn", "svm", "adaboost"]:
try:
input_state = np.concatenate((observation, command), axis=1)
action = self.behaviour_function.predict(input_state)
# print(action)
if np.random.rand() > 0.8:
return int(not np.argmax(action))
return np.argmax(action)
except NotFittedError as e:
return random.randint(0, 1)
def get_greedy_action(self, observation, command):
if self.approximator == "neural_network":
action_probs = self.behaviour_function.predict([observation, command])
action = np.argmax(action_probs)
return action
else:
input_state = np.concatenate((observation, command), axis=1)
action = self.behaviour_function.predict(input_state)
self.testing_state += 1
feature_importances = {}
for t in self.behaviour_function.estimators_:
branch = t.decision_path(input_state).todense()
branch = np.array(branch, dtype=bool)
imp = t.tree_.impurity[branch[0]]
for f, i in zip(t.tree_.feature[branch[0]][:-1], imp[:-1] - imp[1:]):
feature_importances.setdefault(f, []).append(i)
summed_importances = [
sum(feature_importances[0]),
sum(feature_importances[1]),
sum(feature_importances[2]),
sum(feature_importances[3]),
sum(feature_importances[4]),
sum(feature_importances[5]),
]
x = np.arange(len(summed_importances))
plt.figure()
plt.title("Cartpole-v0")
plt.bar(x, summed_importances)
plt.xticks(
x,
[
"feature-1",
"feature-2",
"feature-3",
"feature-4",
r"$d_t^{r}$",
r"$d_t^{h}$",
],
)
plt.savefig("importances_state_" + str(self.testing_state) + ".jpg")
return np.argmax(action)
def train_behaviour_function(self):
random_episodes = self.memory.get_random_samples(self.batch_size)
training_observations = np.zeros((self.batch_size, self.state_size))
training_commands = np.zeros((self.batch_size, 2))
y = []
for idx, episode in enumerate(random_episodes):
T = len(episode["states"])
t1 = np.random.randint(0, T - 1)
t2 = np.random.randint(t1 + 1, T)
state = episode["states"][t1]
desired_return = sum(episode["rewards"][t1:t2])
desired_horizon = t2 - t1
target = episode["actions"][t1]
training_observations[idx] = state[0]
training_commands[idx] = np.asarray(
[
desired_return * self.return_scale,
desired_horizon * self.horizon_scale,
]
)
y.append(target)
_y = keras.utils.to_categorical(y)
if self.approximator == "neural_network":
self.behaviour_function.fit(
[training_observations, training_commands], _y, verbose=0
)
elif self.approximator in ["forest", "extra-trees", "adaboost"]:
input_classifier = np.concatenate(
(training_observations, training_commands), axis=1
)
self.behaviour_function.fit(input_classifier, _y)
def sample_exploratory_commands(self):
best_episodes = self.memory.get_n_best(self.last_few)
exploratory_desired_horizon = np.mean([len(i["states"]) for i in best_episodes])
returns = [i["summed_rewards"] for i in best_episodes]
exploratory_desired_returns = np.random.uniform(
np.mean(returns), np.mean(returns) + np.std(returns)
)
return [exploratory_desired_returns, exploratory_desired_horizon]
def generate_episode(
self, environment, e, desired_return, desired_horizon, testing
):
env = gym.make(environment)
tot_rewards = []
done = False
score = 0
# Gymnasium returns (state,info_dict)
state, _ = env.reset()
scores = []
states = []
actions = []
rewards = []
while not done:
state = np.reshape(state, [1, self.state_size])
states.append(state)
observation = state
command = np.asarray(
[
desired_return * self.return_scale,
desired_horizon * self.horizon_scale,
]
)
command = np.reshape(command, [1, len(command)])
if not testing:
action = self.get_action(observation, command)
actions.append(action)
else:
action = self.get_greedy_action(observation, command)
# Gymnasium returns (s,r,tr,te,info)
next_state, reward, tru, ter, info = env.step(action)
done = tru or ter
next_state = np.reshape(next_state, [1, self.state_size])
rewards.append(reward)
score += reward
state = next_state
desired_return -= reward # Line 8 Algorithm 2
desired_horizon -= 1 # Line 9 Algorithm 2
desired_horizon = np.maximum(desired_horizon, 1)
self.memory.add_sample(states, actions, rewards)
self.testing_rewards.append(score)
if testing:
print("Querying the model ...")
print("Testing score: {}".format(score))
return score
def run_experiment():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--approximator", type=str, default="forest")
parser.add_argument("--environment", type=str, default="CartPole-v0")
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
approximator = args.approximator
environment = args.environment
seed = args.seed
print(args)
episodes = 500
returns = []
agent = UpsideDownAgent(environment, approximator)
epi_bar = trange(episodes)
for e in epi_bar:
for i in range(100):
agent.train_behaviour_function()
for i in range(15):
tmp_r = []
exploratory_commands = (
agent.sample_exploratory_commands()
) # Line 5 Algorithm 1
desired_return = exploratory_commands[0]
desired_horizon = exploratory_commands[1]
r = agent.generate_episode(
environment, e, desired_return, desired_horizon, False
)
tmp_r.append(r)
epi_bar.set_postfix(
{
"mean": np.mean(tmp_r),
"std": np.std(tmp_r),
}
)
# print()
returns.append(np.mean(tmp_r))
exploratory_commands = agent.sample_exploratory_commands()
agent.generate_episode(environment, 1, 200, 200, True)
utils.save_results(environment, approximator, seed, returns)
if approximator == "neural_network":
utils.save_trained_model(environment, seed, agent.behaviour_function)
if __name__ == "__main__":
import warnings
warnings.simplefilter("ignore", DeprecationWarning)
run_experiment()
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