def build(self) :
self.observation = keras.Input(
dtype = tf.float32,
shape = (self.observation_dim,),
)
self.action = build_mlp(self.observation, self.action_dim, self.config.n_layers,
self.config.layer_size, self.config.activation)
self.action_logit = keras.Model(inputs = self.observation, outputs = self.action)
if self.discrete :
sampled_action = tf.squeeze(tf.random.categorical(self.action,1))
else :
self.normal_layer = Normal_action_sample()
sampled_action = self.normal_layer(self.action)
self.sample_action = keras.Model(inputs = self.observation, outputs = sampled_action, name='sample_action')
self.sample_action.summary()
if self.config.use_baseline :
self.baseline_network = BaselineNetwork(self.config, self.observation)
self.baseline_network.build()
def __init__(self, env, config, seed, logger=None):
"""
Initialize Policy Gradient Class
Args:
env: an OpenAI Gym environment
config: class with hyperparameters
logger: logger instance from the logging module
You do not need to implement anything in this function. However,
you will need to use self.discrete, self.observation_dim,
self.action_dim, and self.lr in other methods.
"""
# directory for training outputs
if not os.path.exists(config.output_path):
os.makedirs(config.output_path)
# store hyperparameters
self.config = config
self.seed = seed
self.logger = logger
if logger is None:
self.logger = get_logger(config.log_path)
self.env = env
self.env.seed(self.seed)
# discrete vs continuous action space
self.discrete = isinstance(env.action_space, gym.spaces.Discrete)
self.observation_dim = self.env.observation_space.shape[0]
self.action_dim = self.env.action_space.n if self.discrete else self.env.action_space.shape[
0]
self.lr = self.config.learning_rate
self.init_policy()
if config.use_baseline:
self.baseline_network = BaselineNetwork(env, config)
def build(self):
"""
Build the model by adding all necessary variables.
You don't have to change anything here - we are just calling
all the operations you already defined above to build the tensorflow graph.
"""
# add placeholders
self.add_placeholders_op()
# create policy net
self.build_policy_network_op()
# add square loss
self.add_loss_op()
# add optmizer for the main networks
self.add_optimizer_op()
# add baseline
if self.config.use_baseline:
# check if the baseline is enabled and instantiate the baseline network in that case
self.baseline_network = BaselineNetwork(self.env, self.config, self.observation_placeholder)
self.baseline_network.add_baseline_op()
class PG(object):
"""
Abstract Class for implementing a Policy Gradient Based Algorithm
"""
def __init__(self, env, config, r_seed, logger=None):
"""
Initialize Policy Gradient Class
Args:
env: an OpenAI Gym environment
config: class with hyperparameters
logger: logger instance from the logging module
You do not need to implement anything in this function. However,
you will need to use self.discrete, self.observation_dim,
self.action_dim, and self.lr in other methods.
"""
# directory for training outputs
if not os.path.exists(config.output_path):
os.makedirs(config.output_path)
# store hyperparameters
self.config = config
self.r_seed = r_seed
self.logger = logger
if logger is None:
self.logger = get_logger(config.log_path)
self.env = env
self.env.seed(self.r_seed)
# discrete vs continuous action space
self.discrete = isinstance(env.action_space, gym.spaces.Discrete)
self.observation_dim = self.env.observation_space.shape[0]
self.action_dim = self.env.action_space.n if self.discrete else self.env.action_space.shape[
0]
self.lr = self.config.learning_rate
# build model
self.build()
def add_placeholders_op(self):
"""
Add placeholders for observation, action, and advantage:
self.observation_placeholder, type: tf.float32
self.action_placeholder, type: depends on the self.discrete
self.advantage_placeholder, type: tf.float32
HINT: Check self.observation_dim and self.action_dim
HINT: In the case of continuous action space, an action will be specified by
'self.action_dim' float32 numbers (i.e. a vector with size 'self.action_dim')
"""
#######################################################
######### YOUR CODE HERE - 4-6 lines. ############
obs_dim, action_dim = self.observation_dim, self.action_dim
self.observation_placeholder = tf.placeholder(tf.float32,
shape=(None, obs_dim),
name="obs")
if self.discrete:
self.action_placeholder = tf.placeholder(tf.int32,
shape=(None),
name="action")
else:
self.action_placeholder = tf.placeholder(tf.float32,
shape=(None, action_dim),
name="action")
self.advantage_placeholder = tf.placeholder(tf.float32,
shape=(None,),
name="advantage")
# TODO Double check shape of advantage placeholder
#######################################################
######### END YOUR CODE. ############
def build_policy_network_op(self, scope="policy_network"):
"""
Build the policy network, construct the tensorflow operation to sample
actions from the policy network outputs, and compute the log probabilities
of the actions taken (for computing the loss later). These operations are
stored in self.sampled_action and self.logprob. Must handle both settings
of self.discrete.
Args:
scope: the scope of the neural network
TODO:
Discrete case:
action_logits: the logits for each action
HINT: use build_mlp, check self.config for layer_size and
n_layers
self.sampled_action: sample from these logits
HINT: use tf.multinomial + tf.squeeze
self.logprob: compute the log probabilities of the taken actions
HINT: 1. tf.nn.sparse_softmax_cross_entropy_with_logits computes
the *negative* log probabilities of labels, given logits.
2. taken actions are different than sampled actions!
Continuous case:
To build a policy in a continuous action space domain, we will have the
model output the means of each action dimension, and then sample from
a multivariate normal distribution with these means and trainable standard
deviation.
That is, the action a_t ~ N( mu(o_t), sigma)
where mu(o_t) is the network that outputs the means for each action
dimension, and sigma is a trainable variable for the standard deviations.
N here is a multivariate gaussian distribution with the given parameters.
action_means: the predicted means for each action dimension.
HINT: use build_mlp, check self.config for layer_size and
n_layers
log_std: a trainable variable for the log standard deviations.
HINT: think about why we use log std as the trainable variable instead of std
HINT: use tf.get_variable
HINT: The shape of this should match the shape of action dimension
self.sampled_action: sample from the gaussian distribution as described above
HINT: use tf.random_normal
HINT: use re-parametrization to obtain N(mu, sigma) from N(0, 1)
self.lobprob: the log probabilities of the taken actions
HINT: use tf.contrib.distributions.MultivariateNormalDiag
"""
#######################################################
######### YOUR CODE HERE - 8-12 lines. ############
self.scope = scope
if self.discrete:
action_logits = build_mlp(self.observation_placeholder,
self.action_dim, self.scope,
self.config.n_layers,
self.config.layer_size,
output_activation=self.config.activation)
#self.sampled_action = tf.multinomial(action_logits, 1)
self.sampled_action = tf.squeeze(tf.multinomial(action_logits, 1),1)
self.logprob = -tf.nn.sparse_softmax_cross_entropy_with_logits(
#labels=self.action_placeholder,
labels=self.action_placeholder,
logits=action_logits)
else:
action_means = build_mlp(self.observation_placeholder,
self.action_dim, self.scope,
self.config.n_layers,
self.config.layer_size)
log_std = tf.get_variable("log_std")
self.sampled_action = action_means + tf.multiply(
log_std.exp(), tf.random_normal(self.batch_size))
self.lobprob = tf.log(
tf.contrib.distributions.MultivariateNormalDiag())
#######################################################
######### END YOUR CODE. ############
def add_loss_op(self):
"""
Compute the loss, averaged for a given batch.
Recall the update for REINFORCE with advantage:
θ = θ + α ∇_θ log π_θ(a_t|s_t) A_t
Think about how to express this update as minimizing a
loss (so that tensorflow will do the gradient computations
for you).
You only have to reference fields of 'self' that have already
been set in the previous methods.
Save the loss as self.loss
"""
######################################################
######### YOUR CODE HERE - 1-2 lines. ############
self.loss = -tf.reduce_mean(self.advantage_placeholder * self.logprob)
# TODO
#######################################################
######### END YOUR CODE. ############
def add_optimizer_op(self):
"""
Set 'self.train_op' using AdamOptimizer
HINT: Use self.lr, and minimize self.loss
"""
######################################################
######### YOUR CODE HERE - 1-2 lines. ############
self.train_op = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.loss)
# TODO
#######################################################
######### END YOUR CODE. ############
def build(self):
"""
Build the model by adding all necessary variables.
You don't have to change anything here - we are just calling
all the operations you already defined above to build the tensorflow graph.
"""
# add placeholders
self.add_placeholders_op()
# create policy net
self.build_policy_network_op()
# add square loss
self.add_loss_op()
# add optmizer for the main networks
self.add_optimizer_op()
# add baseline
if self.config.use_baseline:
# check if the baseline is enabled and instantiate the baseline network in that case
self.baseline_network = BaselineNetwork(
self.env, self.config, self.observation_placeholder)
self.baseline_network.add_baseline_op()
def initialize(self):
"""
Assumes the graph has been constructed (have called self.build())
Creates a tf Session and run initializer of variables
You don't have to change or use anything here.
"""
# setting the seed
#pdb.set_trace()
# create tf session
self.sess = tf.Session()
# tensorboard stuff
self.add_summary()
# initiliaze all variables
init = tf.global_variables_initializer()
self.sess.run(init)
if self.config.use_baseline:
self.baseline_network.set_session(self.sess)
def add_summary(self):
"""
Tensorboard stuff.
You don't have to change or use anything here.
"""
# extra placeholders to log stuff from python
self.avg_reward_placeholder = tf.placeholder(tf.float32,
shape=(),
name="avg_reward")
self.max_reward_placeholder = tf.placeholder(tf.float32,
shape=(),
name="max_reward")
self.std_reward_placeholder = tf.placeholder(tf.float32,
shape=(),
name="std_reward")
self.eval_reward_placeholder = tf.placeholder(tf.float32,
shape=(),
name="eval_reward")
# extra summaries from python -> placeholders
tf.summary.scalar("Avg Reward", self.avg_reward_placeholder)
tf.summary.scalar("Max Reward", self.max_reward_placeholder)
tf.summary.scalar("Std Reward", self.std_reward_placeholder)
tf.summary.scalar("Eval Reward", self.eval_reward_placeholder)
# logging
self.merged = tf.summary.merge_all()
self.file_writer = tf.summary.FileWriter(self.config.output_path,
self.sess.graph)
def init_averages(self):
"""
Defines extra attributes for tensorboard.
You don't have to change or use anything here.
"""
self.avg_reward = 0.
self.max_reward = 0.
self.std_reward = 0.
self.eval_reward = 0.
def update_averages(self, rewards, scores_eval):
"""
Update the averages.
You don't have to change or use anything here.
Args:
rewards: deque
scores_eval: list
"""
self.avg_reward = np.mean(rewards)
self.max_reward = np.max(rewards)
self.std_reward = np.sqrt(np.var(rewards) / len(rewards))
if len(scores_eval) > 0:
self.eval_reward = scores_eval[-1]
def record_summary(self, t):
"""
Add summary to tensorboard
You don't have to change or use anything here.
"""
fd = {
self.avg_reward_placeholder: self.avg_reward,
self.max_reward_placeholder: self.max_reward,
self.std_reward_placeholder: self.std_reward,
self.eval_reward_placeholder: self.eval_reward,
}
summary = self.sess.run(self.merged, feed_dict=fd)
# tensorboard stuff
self.file_writer.add_summary(summary, t)
def sample_path(self, env, num_episodes=None):
"""
Sample paths (trajectories) from the environment.
Args:
num_episodes: the number of episodes to be sampled
if none, sample one batch (size indicated by config file)
env: open AI Gym envinronment
Returns:
paths: a list of paths. Each path in paths is a dictionary with
path["observation"] a numpy array of ordered observations in the path
path["actions"] a numpy array of the corresponding actions in the path
path["reward"] a numpy array of the corresponding rewards in the path
total_rewards: the sum of all rewards encountered during this "path"
You do not have to implement anything in this function, but you will need to
understand what it returns, and it is worthwhile to look over the code
just so you understand how we are taking actions in the environment
and generating batches to train on.
"""
episode = 0
episode_rewards = []
paths = []
t = 0
while (num_episodes or t < self.config.batch_size):
state = env.reset()
states, actions, rewards = [], [], []
episode_reward = 0
for step in range(self.config.max_ep_len):
states.append(state)
action = self.sess.run(
self.sampled_action,
feed_dict={self.observation_placeholder:
states[-1][None]})[0]
state, reward, done, info = env.step(action)
actions.append(action)
rewards.append(reward)
episode_reward += reward
t += 1
if (done or step == self.config.max_ep_len - 1):
episode_rewards.append(episode_reward)
break
if (not num_episodes) and t == self.config.batch_size:
break
path = {
"observation": np.array(states),
"reward": np.array(rewards),
"action": np.array(actions)}
paths.append(path)
episode += 1
if num_episodes and episode >= num_episodes:
break
return paths, episode_rewards
def get_returns(self, paths):
"""
Calculate the returns G_t for each timestep
Args:
paths: recorded sample paths. See sample_path() for details.
Return:
returns: return G_t for each timestep
After acting in the environment, we record the observations, actions, and
rewards. To get the advantages that we need for the policy update, we have
to convert the rewards into returns, G_t, which are themselves an estimate
of Q^π (s_t, a_t):
G_t = r_t + γ r_{t+1} + γ^2 r_{t+2} + ... + γ^{T-t} r_T
where T is the last timestep of the episode.
Note that here we are creating a list of returns for each path
TODO: compute and return G_t for each timestep. Use self.config.gamma.
"""
all_returns = []
for path in paths:
rewards = path["reward"]
#######################################################
######### YOUR CODE HERE - 5-10 lines. ############
returns = []
previous_return = 0
for t in range(rewards.shape[0] - 1, -1, -1):
current_return = rewards[
t] + self.config.gamma * previous_return
returns.append(current_return)
previous_return = current_return
# reverse the list
returns = returns[::-1]
#######################################################
######### END YOUR CODE. ############
all_returns.append(returns)
returns = np.concatenate(all_returns)
return returns
def normalize_advantage(self, advantages):
"""
Normalizes the advantage. This function is called only if self.config.normalize_advantage is True.
Args:
advantages: the advantages
Returns:
adv: Normalized Advantage
Calculate the advantages, by normalizing the advantages.
TODO:
Normalize the advantages so that they have a mean of 0 and standard deviation of 1.
"""
#######################################################
######### YOUR CODE HERE - 1-5 lines. ############
advantages -= tf.reduce_mean(advantages)
advantages /= tf.math.reduce_std(advantages)
#######################################################
######### END YOUR CODE. ############
return advantages
def calculate_advantage(self, returns, observations):
"""
Calculates the advantage for each of the observations
Args:
returns: the returns
observations: the observations
Returns:
advantage: the advantage
"""
if self.config.use_baseline:
# override the behavior of advantage by subtracting baseline
advantages = self.baseline_network.calculate_advantage(
returns, observations)
else:
advantages = returns
if self.config.normalize_advantage:
advantages = self.normalize_advantage(advantages)
return advantages
def train(self):
"""
Performs training
You do not have to change or use anything here, but take a look
to see how all the code you've written fits together!
"""
last_eval = 0
last_record = 0
scores_eval = []
self.init_averages()
scores_eval = [] # list of scores computed at iteration time
for t in range(self.config.num_batches):
# collect a minibatch of samples
paths, total_rewards = self.sample_path(self.env)
scores_eval = scores_eval + total_rewards
observations = np.concatenate(
[path["observation"] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
# compute Q-val estimates (discounted future returns) for each time step
returns = self.get_returns(paths)
# advantage will depend on the baseline implementation
advantages = self.calculate_advantage(returns, observations)
# run training operations
if self.config.use_baseline:
self.baseline_network.update_baseline(returns, observations)
self.sess.run(self.train_op,
feed_dict={
self.observation_placeholder: observations,
self.action_placeholder: actions,
self.advantage_placeholder: advantages
})
# tf stuff
if (t % self.config.summary_freq == 0):
self.update_averages(total_rewards, scores_eval)
self.record_summary(t)
# compute reward statistics for this batch and log
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
if self.config.record and (last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
self.logger.info("- Training done.")
export_plot(scores_eval, "Score", self.config.env_name,
self.config.plot_output)
def evaluate(self, env=None, num_episodes=1):
"""
Evaluates the return for num_episodes episodes.
Not used right now, all evaluation statistics are computed during training
episodes.
"""
if env == None: env = self.env
paths, rewards = self.sample_path(env, num_episodes)
avg_reward = np.mean(rewards)
sigma_reward = np.sqrt(np.var(rewards) / len(rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
return avg_reward
def record(self):
"""
Recreate an env and record a video for one episode
"""
env = gym.make(self.config.env_name)
env.seed(self.r_seed)
env = gym.wrappers.Monitor(env,
self.config.record_path,
video_callable=lambda x: True,
resume=True)
self.evaluate(env, 1)
def run(self):
"""
Apply procedures of training for a PG.
"""
# initialize
self.initialize()
# record one game at the beginning
if self.config.record:
self.record()
# model
self.train()
# record one game at the end
if self.config.record:
self.record()
class PG() :
def __init__(self,env : gym.Env, config, r_seed) :
if not os.path.exists(config.output_path) :
os.makedirs(config.output_path)
self.config = config
self.r_seed = r_seed
self.env = env
self.env.seed(self.r_seed)
self.discrete = isinstance(env.action_space, gym.spaces.Discrete)
self.observation_dim = self.env.observation_space.shape[0]
self.action_dim = self.env.action_space.n if self.discrete else self.env.action_space.shape[0]
self.lr = self.config.learning_rate
self.build()
self.set_summary()
self.set_optimizer()
def build(self) :
self.observation = keras.Input(
dtype = tf.float32,
shape = (self.observation_dim,),
)
self.action = build_mlp(self.observation, self.action_dim, self.config.n_layers,
self.config.layer_size, self.config.activation)
self.action_logit = keras.Model(inputs = self.observation, outputs = self.action)
if self.discrete :
sampled_action = tf.squeeze(tf.random.categorical(self.action,1))
else :
self.normal_layer = Normal_action_sample()
sampled_action = self.normal_layer(self.action)
self.sample_action = keras.Model(inputs = self.observation, outputs = sampled_action, name='sample_action')
self.sample_action.summary()
if self.config.use_baseline :
self.baseline_network = BaselineNetwork(self.config, self.observation)
self.baseline_network.build()
def set_optimizer(self) :
self.optimizer = keras.optimizers.Adam()
def loss_func(self, observations, actions, advantages) :
if self.discrete :
self.logprob = -tf.nn.sparse_softmax_cross_entropy_with_logits(actions, self.action_logit(observations))
else :
self.logprob = tfp.distributions.MultivariateNormalDiag(
loc = self.action_logit(observations),
scale_diag= tf.math.exp(self.normal_layer.log_std),
).log_prob(actions)
advantages = tf.cast(advantages, tf.float32)
return -tf.math.multiply(self.logprob, advantages)
def train_step(self, observations, actions, advantages) :
with tf.GradientTape() as tape :
self.loss = self.loss_func(observations, actions, advantages)
gradients = tape.gradient(self.loss, self.sample_action.trainable_variables)
self.optimizer.learning_rate = self.lr
self.optimizer.apply_gradients(zip(gradients, self.sample_action.trainable_variables))
def set_summary(self) :
self.file_writer = tf.summary.create_file_writer(self.config.output_path)
self.file_writer.set_as_default()
def get_returns(self, paths) :
all_returns = []
for path in paths :
rewards = path["reward"]
gammas = self.config.gamma**np.arange(len(rewards))
returns = np.flip(np.cumsum(np.flip(rewards * gammas)))
rev_gammas = self.config.gamma**(-np.arange(len(rewards)))
returns = rev_gammas * returns
all_returns.append(returns)
returns = np.concatenate(all_returns)
return returns
def normalize_advantage(self, advantages) :
adv = (advantages - tf.math.reduce_mean(advantages))/tfp.stats.stddev(advantages)
return adv
def calculate_advantage(self, returns, observations) :
if self.config.use_baseline :
advantages = self.baseline_network.calculate_advantage(returns, observations)
else :
advantages = returns
if self.config.normalize_advantage :
advantages = self.normalize_advantage(advantages)
return advantages
def add_sumary(self, t) :
tf.summary.scalar('Avg Reward', self.avg_reward, step= t)
tf.summary.scalar('Max Reward', self.max_reward, step= t)
tf.summary.scalar('Std Reward', self.std_reward, step= t)
tf.summary.scalar('Eval Reward', self.eval_reward, step= t)
def init_averages(self) :
self.avg_reward = 0
self.max_reward = 0
self.std_reward = 0
self.eval_reward = 0
def update_averages(self, rewards, scores_eval) :
self.avg_reward = np.mean(rewards)
self.max_reward = np.max(rewards)
self.std_reward = np.sqrt(np.var(rewards)/ len(rewards))
if len(scores_eval) > 0 :
self.eval_reward = scores_eval[-1]
def sample_path(self, env, num_episodes = None) :
episode = 0
episode_rewards = []
paths = []
t = 0
while (num_episodes or t < self.config.batch_size) :
state = env.reset()
states, actions, rewards = [], [], []
episode_reward = 0
for step in range(self.config.max_ep_len) :
states.append(state)
action = self.sample_action(states[-1][None])
if self.discrete :
action = int(action)
else :
action = action[0]
state, reward, done, info = env.step(action)
# env.render()
actions.append(action)
rewards.append(reward)
episode_reward += reward
t += 1
if (done or step == self.config.max_ep_len-1) :
episode_rewards.append(episode_reward)
break
if (not num_episodes) and t == self.config.batch_size :
break
path = {
'observation' : np.array(states),
'reward' : np.array(rewards),
'action' : np.array(actions)
}
paths.append(path)
episode += 1
if num_episodes and episode >= num_episodes :
break
return paths, episode_rewards
def train(self) :
last_eval = 0
last_record = 0
scores_eval = []
self.init_averages()
for t in range(self.config.num_batches) :
paths, total_rewards = self.sample_path(self.env)
scores_eval = scores_eval + total_rewards
observations = np.concatenate([path['observation'] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
returns = self.get_returns(paths)
advantages = self.calculate_advantage(returns, observations)
if self.config.use_baseline :
self.baseline_network.update_baseline(returns, observations)
self.train_step(observations, actions, advantages)
if (t % self.config.summary_freq == 0) :
self.update_averages(total_rewards, scores_eval)
self.add_sumary(t)
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards)/len(total_rewards))
sys.stdout.write('\r')
sys.stdout.flush()
msg = "Average reward: {0:04.2f} +/- {1:04.2f} step:{2}/{3} ".format(avg_reward, sigma_reward,
t, self.config.num_batches)
print(msg, end='')
if self.config.record and not ((t+1)% self.config.record_freq):
sys.stdout.write('\n')
sys.stdout.flush()
print('Recording')
self.record()
sys.stdout.write('\n')
sys.stdout.flush()
print('Training done.')
print(self.normal_layer.log_std.numpy())
export_plot(scores_eval, 'Score', self.config.env_name, self.config.plot_output)
def evaluate(self, env= None, num_episodes=1):
if env==None : env = self.env
self.sample_path(env, num_episodes)
def record(self) :
env = gym.make(self.config.env_name)
env.seed(self.r_seed)
env = gym.wrappers.Monitor(env, self.config.record_path, video_callable= lambda x: True, resume=True)
self.evaluate(env, 1)
def run(self) :
if self.config.record :
self.record()
self.train()
if self.config.record :
self.record()
class PolicyGradient(object):
"""
Class for implementing a policy gradient algorithm
"""
def __init__(self, env, config, seed, logger=None):
"""
Initialize Policy Gradient Class
Args:
env: an OpenAI Gym environment
config: class with hyperparameters
logger: logger instance from the logging module
You do not need to implement anything in this function. However,
you will need to use self.discrete, self.observation_dim,
self.action_dim, and self.lr in other methods.
"""
# directory for training outputs
if not os.path.exists(config.output_path):
os.makedirs(config.output_path)
# store hyperparameters
self.config = config
self.seed = seed
self.logger = logger
if logger is None:
self.logger = get_logger(config.log_path)
self.env = env
self.env.seed(self.seed)
# discrete vs continuous action space
self.discrete = isinstance(env.action_space, gym.spaces.Discrete)
self.observation_dim = self.env.observation_space.shape[0]
self.action_dim = self.env.action_space.n if self.discrete else self.env.action_space.shape[
0]
self.lr = self.config.learning_rate
self.init_policy()
if config.use_baseline:
self.baseline_network = BaselineNetwork(env, config)
def init_policy(self):
"""
Please do the following:
1. Create a network using build_mlp. It should map vectors of size
self.observation_dim to vectors of size self.action_dim, and use
the number of layers and layer size from self.config
2. If self.discrete = True (meaning that the actions are discrete, i.e.
from the set {0, 1, ..., N-1} where N is the number of actions),
instantiate a CategoricalPolicy.
If self.discrete = False (meaning that the actions are continuous,
i.e. elements of R^d where d is the dimension), instantiate a
GaussianPolicy. Either way, assign the policy to self.policy
3. Create an optimizer for the policy, with learning rate self.lr
Note that the policy is an instance of (a subclass of) nn.Module, so
you can call the parameters() method to get its parameters.
"""
#######################################################
######### YOUR CODE HERE - 8-12 lines. ############
#######################################################
######### END YOUR CODE. ############
def init_averages(self):
"""
You don't have to change or use anything here.
"""
self.avg_reward = 0.
self.max_reward = 0.
self.std_reward = 0.
self.eval_reward = 0.
def update_averages(self, rewards, scores_eval):
"""
Update the averages.
You don't have to change or use anything here.
Args:
rewards: deque
scores_eval: list
"""
self.avg_reward = np.mean(rewards)
self.max_reward = np.max(rewards)
self.std_reward = np.sqrt(np.var(rewards) / len(rewards))
if len(scores_eval) > 0:
self.eval_reward = scores_eval[-1]
def record_summary(self, t):
pass
def sample_path(self, env, num_episodes=None):
"""
Sample paths (trajectories) from the environment.
Args:
num_episodes: the number of episodes to be sampled
if none, sample one batch (size indicated by config file)
env: open AI Gym envinronment
Returns:
paths: a list of paths. Each path in paths is a dictionary with
path["observation"] a numpy array of ordered observations in the path
path["actions"] a numpy array of the corresponding actions in the path
path["reward"] a numpy array of the corresponding rewards in the path
total_rewards: the sum of all rewards encountered during this "path"
You do not have to implement anything in this function, but you will need to
understand what it returns, and it is worthwhile to look over the code
just so you understand how we are taking actions in the environment
and generating batches to train on.
"""
episode = 0
episode_rewards = []
paths = []
t = 0
while (num_episodes or t < self.config.batch_size):
state = env.reset()
states, actions, rewards = [], [], []
episode_reward = 0
for step in range(self.config.max_ep_len):
states.append(state)
action = self.policy.act(states[-1][None])[0]
state, reward, done, info = env.step(action)
actions.append(action)
rewards.append(reward)
episode_reward += reward
t += 1
if (done or step == self.config.max_ep_len - 1):
episode_rewards.append(episode_reward)
break
if (not num_episodes) and t == self.config.batch_size:
break
path = {
"observation": np.array(states),
"reward": np.array(rewards),
"action": np.array(actions)
}
paths.append(path)
episode += 1
if num_episodes and episode >= num_episodes:
break
return paths, episode_rewards
def get_returns(self, paths):
"""
Calculate the returns G_t for each timestep
Args:
paths: recorded sample paths. See sample_path() for details.
Return:
returns: return G_t for each timestep
After acting in the environment, we record the observations, actions, and
rewards. To get the advantages that we need for the policy update, we have
to convert the rewards into returns, G_t, which are themselves an estimate
of Q^π (s_t, a_t):
G_t = r_t + γ r_{t+1} + γ^2 r_{t+2} + ... + γ^{T-t} r_T
where T is the last timestep of the episode.
Note that here we are creating a list of returns for each path
TODO: compute and return G_t for each timestep. Use self.config.gamma.
"""
all_returns = []
for path in paths:
rewards = path["reward"]
#######################################################
######### YOUR CODE HERE - 5-10 lines. ############
#######################################################
######### END YOUR CODE. ############
all_returns.append(returns)
returns = np.concatenate(all_returns)
return returns
def normalize_advantage(self, advantages):
"""
Args:
advantages: np.array of shape [batch size]
Returns:
normalized_advantages: np.array of shape [batch size]
TODO:
Normalize the advantages so that they have a mean of 0 and standard
deviation of 1. Put the result in a variable called
normalized_advantages (which will be returned).
Note:
This function is called only if self.config.normalize_advantage is True.
"""
#######################################################
######### YOUR CODE HERE - 1-2 lines. ############
#######################################################
######### END YOUR CODE. ############
return normalized_advantages
def calculate_advantage(self, returns, observations):
"""
Calculates the advantage for each of the observations
Args:
returns: np.array of shape [batch size]
observations: np.array of shape [batch size, dim(observation space)]
Returns:
advantages: np.array of shape [batch size]
"""
if self.config.use_baseline:
# override the behavior of advantage by subtracting baseline
advantages = self.baseline_network.calculate_advantage(
returns, observations)
else:
advantages = returns
if self.config.normalize_advantage:
advantages = self.normalize_advantage(advantages)
return advantages
def update_policy(self, observations, actions, advantages):
"""
Args:
observations: np.array of shape [batch size, dim(observation space)]
actions: np.array of shape
[batch size, dim(action space)] if continuous
[batch size] (and integer type) if discrete
advantages: np.array of shape [batch size]
Perform one update on the policy using the provided data.
To compute the loss, you will need the log probabilities of the actions
given the observations. Note that the policy's action_distribution
method returns an instance of a subclass of
torch.distributions.Distribution, and that object can be used to
compute log probabilities.
See https://pytorch.org/docs/stable/distributions.html#distribution
Note:
PyTorch optimizers will try to minimize the loss you compute, but you
want to maximize the policy's performance.
"""
observations = np2torch(observations)
actions = np2torch(actions)
advantages = np2torch(advantages)
#######################################################
######### YOUR CODE HERE - 5-7 lines. ############
#######################################################
######### END YOUR CODE. ############
def train(self):
"""
Performs training
You do not have to change or use anything here, but take a look
to see how all the code you've written fits together!
"""
last_record = 0
self.init_averages()
all_total_rewards = [
] # the returns of all episodes samples for training purposes
averaged_total_rewards = [] # the returns for each iteration
for t in range(self.config.num_batches):
# collect a minibatch of samples
paths, total_rewards = self.sample_path(self.env)
all_total_rewards.extend(total_rewards)
observations = np.concatenate(
[path["observation"] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
# compute Q-val estimates (discounted future returns) for each time step
returns = self.get_returns(paths)
# advantage will depend on the baseline implementation
advantages = self.calculate_advantage(returns, observations)
# run training operations
if self.config.use_baseline:
self.baseline_network.update_baseline(returns, observations)
self.update_policy(observations, actions, advantages)
# logging
if (t % self.config.summary_freq == 0):
self.update_averages(total_rewards, all_total_rewards)
self.record_summary(t)
# compute reward statistics for this batch and log
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
averaged_total_rewards.append(avg_reward)
self.logger.info(msg)
if self.config.record and (last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
self.logger.info("- Training done.")
np.save(self.config.scores_output, averaged_total_rewards)
export_plot(averaged_total_rewards, "Score", self.config.env_name,
self.config.plot_output)
def evaluate(self, env=None, num_episodes=1):
"""
Evaluates the return for num_episodes episodes.
Not used right now, all evaluation statistics are computed during training
episodes.
"""
if env == None: env = self.env
paths, rewards = self.sample_path(env, num_episodes)
avg_reward = np.mean(rewards)
sigma_reward = np.sqrt(np.var(rewards) / len(rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
return avg_reward
def record(self):
"""
Recreate an env and record a video for one episode
"""
env = gym.make(self.config.env_name)
env.seed(self.seed)
env = gym.wrappers.Monitor(env,
self.config.record_path,
video_callable=lambda x: True,
resume=True)
self.evaluate(env, 1)
def run(self):
"""
Apply procedures of training for a PG.
"""
# record one game at the beginning
if self.config.record:
self.record()
# model
self.train()
# record one game at the end
if self.config.record:
self.record()