total_loss = actor_loss + critic_loss
gradient = tape.gradient(total_loss, self.actor_critic.trainable_variables)
self.actor_critic.optimizer.apply_gradients(zip(
gradient, self.actor_critic.trainable_variables))
############## PART 3 ###################
import gym
import numpy as np
from actor_critic import Agent
from utils import plot_learning_curve
if __name__=='main':
env = gym.make('CartPole-v0')
agent = Agent(alpha=1e-5, n_actions=env.action_space.n)
n_games = 1800
filename='cartpole.png'
figure_file = 'plots/' + filename
best_score = env.reward_range[0]
score_history = []
load_checkpoint
if load_checkpoint:
agent.load_models()
for i in range(n_games):
observation = env.reset()
done = False
import gym
import numpy as np
from actor_critic import Agent
from utils import plotLearning
if __name__ == '__main__':
env = gym.make('CartPole-v0')
agent = Agent(alpha=0.001, n_actions=env.action_space.n)
n_episodes = 400
filename = 'cartpole.png'
best_score = env.reward_range[0]
score_history = []
load_checkpoint = False
if load_checkpoint:
agent.load_model()
for i in range(n_episodes):
obs = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(obs)
new_state, reward, done, info = env.step(action)
score += reward
if not load_checkpoint:
agent.learn(obs, reward, new_state, done)
obs = new_state
score_history.append(score)
avg_score = np.mean(score_history[-100:])
print(f'episode-{i},score={score},avg-score={avg_score}')
import gym
import numpy as np
from actor_critic import Agent
from utils import plot_learning_curve
if __name__ == '__main__':
env = gym.make('CartPole-v0')
agent = Agent(alpha=1e-5, n_actions=env.action_space.n)
n_games = 1800
filename = 'cartpole.png'
figure_file = 'plots/' + filename
best_score = env.reward_range[0]
score_history = []
load_checkpoint = False
if load_checkpoint:
agent.load_model()
for i in range(n_games):
state = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(state)
next_state, reward, done, info = env.step(action)
score += reward
import pybullet_envs
import gym
import numpy as np
from actor_critic import Agent
from utils import plot_learning_curve
from gym import wrappers
if __name__ == "__main__":
env_name = "InvertedPendulumBulletEnv-v0"
env = gym.make(env_name)
agent = Agent(
input_dims=env.observation_space.shape,
env=env,
n_actions=env.action_space.shape[0],
)
n_games = 250
env = wrappers.Monitor(env,
"tmp/video",
video_callable=lambda episode_id: True,
force=True)
filename = "inverted_pendulum.png"
figure_file = "plots/" + filename
best_score = env.reward_range[0]
score_history = []
load_checkpoint = False
if load_checkpoint:
import gym
import numpy as np
from actor_critic import Agent, plot_learning_curve
if __name__ == '__main__':
env = gym.make('LunarLander-v2')
best_score = -np.inf
load_checkpoint = True
agent = Agent(gamma=0.99,
lr=5e-6,
input_dims=[8],
n_actions=4,
fc1_dims=2048,
fc2_dims=1536,
chkpt_dir='.\\models\\',
env_name='Lunar_Practice')
if load_checkpoint:
agent.load_model()
n_games = 5
fname = 'ACTOR_CRITIC_' + 'lunar_practice'
figure_file = '.\\plots\\' + fname + '.png'
scores = []
for i in range(n_games):
done = False
observation = env.reset()
score = 0
while not done:
running_avg[i] = np.mean(scores[max(0, i - 100):i + 1])
plt.plot(x, running_avg)
plt.title('running average-100 games')
plt.savefig(fig_file)
if __name__ == '__main__':
env = gym.make('LunarLander-v2')
n_games = 2000
lr = 0.00005
fc1 = 2048
fc2 = 1536
gamma = 0.99
fname = f'plots/lunarlander_actor_critic_{fc1}_{fc2}_lr{lr}_{n_games}games.png'
scores = []
agent = Agent(4, [8], lr=lr, fc1=fc1, fc2=fc2, gamma=gamma)
for i in range(n_games):
observation = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.learn(observation, reward, observation_, done)
observation = observation_
scores.append(score)
avg = np.mean(scores[-100:])
print(f'episode:{i}, score:{score}, average:{avg}')
x = [(i + 1) for i in len(range(scores))]
plot_learning(x, scores, fname)