def __init__(self,
input_dim,
num_actions,
network_params=None,
set_device=None,
gradient_clipping_norm=None,
reward_to_go=True,
learning_rate=0.01,
seed=1364):
self.seed = seed
# Training parameters
self.gamma = 0.99
self.total_steps_so_far = 0
self.save_model_frequency = 100
self.learning_rate = learning_rate
self.latest_learning_rate = learning_rate
self.gradient_clipping_norm = gradient_clipping_norm
# Experience Replay Memory
self.memory_size = 40000
self.replay_memory = deque([], maxlen=self.memory_size)
# ----------------------------------------
# Make the algorithm outputs reproducible
make_deterministic(seed)
# ----------------------------------------
# if gpu is to be used
if set_device is None:
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
# self.device = "cuda" if torch.cuda.is_available() else "cpu"
else:
self.device = torch.device(set_device)
# Policy gradient parameters
self.episode_return_batch = torch.Tensor()
self.actions_batch = torch.Tensor()
self.log_probs_batch = torch.Tensor()
self.reward_to_go = reward_to_go
self.batch_size = 4096 # Batch size for Policy Gradient should be large to reduce variance
# (Explanation of the network_params in networks/network_builder.py)
if network_params is None:
network_params = {
'input_dim': input_dim,
'conv_layers': [(3, 16, 5, 2), (16, 32, 5, 2), (32, 32, 5, 2)],
'dense_layers': [num_actions],
'conv_bn': True,
'activation': 'relu'
}
self.policy_net = CreateNet(network_params).to(self.device)
# self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=self.learning_rate)
self.optimizer = optim.Adam(self.policy_net.parameters(),
lr=self.learning_rate,
eps=1e-4)
def run_single_episode(self, env, episode=None):
# Make each episode deterministic based on the total_iteration_number
make_deterministic(self.total_steps_so_far, env.env)
finished = False
episode_rewards = []
log_probs = []
# Create the first state of the episode
state_1 = env.get_state(episode_start=True)
while not finished:
action, log_prob = self.get_action_and_log_prob(state_1)
# Take the selected action in the environment
_, reward, finished, _ = env.env.step(action)
episode_rewards.append(reward)
log_probs.append(log_prob)
# If not finished, set the current_state as previous state
if not finished:
state_1 = env.get_state()
# One single training iteration is passed
self.total_steps_so_far += 1
# If the agent has received a satisfactory episode reward, stop it.
if sum(episode_rewards) >= env.score_required_to_win:
finished = True
ep_len = len(episode_rewards)
# Computing episode return for all time points in the episode (G_t)
# input: vector [x0, x1, x2], output: [x0 + discount * x1 + (discount ^ 2) * x2,
# x1 + discount * x2,
# x2]
if self.reward_to_go:
episode_return = torch.tensor([
sum(episode_rewards[i:] * (self.gamma**np.arange(ep_len - i)))
for i in range(ep_len)
])
else:
episode_return = torch.ones(ep_len) * sum(episode_rewards)
self.episode_return_batch = torch.cat(
[self.episode_return_batch, episode_return])
self.log_probs_batch = torch.cat(
[self.log_probs_batch, torch.cat(log_probs)])
# Policy Network optimisation:
# ----------------------------
if len(self.episode_return_batch) >= self.batch_size:
_ = self.learning_step()
# Return the total rewards collected within this single episode run
return episode_rewards
def run_single_episode(self, env, episode):
# Make each episode deterministic based on the total_iteration_number
make_deterministic(self.total_steps_so_far, env.env)
finished = False
episode_rewards = []
episode_losses = []
# Create the first state of the episode
state_1 = env.get_state(episode_start=True)
while not finished:
action_1 = self.get_action(env, state_1)
# Take the selected action in the environment
s2, reward_1, finished, _ = env.env.step(action_1)
# when episode is finished, state_2 does not matter,
# and won't contribute to the optimisation
# (because state_1 was the last state of the episode)
state_2 = (0 * state_1) if finished else env.get_state()
# Add the current transition (s, a, r, s', done) to the replay memory
self.add_experience_to_replay_memory(state_1, action_1, reward_1,
state_2, finished)
# Policy Network optimisation:
# ----------------------------
# If there are enough sample transitions inside the replay_memory,
# then we can start training our policy network using them;
# Otherwise we move on to the next state of the episode.
if len(self.replay_memory) >= self.batch_size:
# Take a random sample minibatch from the replay memory
minibatch = self.sample_from_replay_memory(self.batch_size)
# Compute the TD loss over the minibatch
loss = self.learning_step(minibatch)
# Track the value of loss (for debugging purpose)
episode_losses.append(loss.item())
# Go to the next step of the episode
state_1 = state_2
# Add up the rewards collected during this episode
episode_rewards.append(reward_1)
# One single training iteration is passed
self.total_steps_so_far += 1
# If the agent has received a satisfactory episode reward, stop it.
if sum(episode_rewards) >= env.score_required_to_win:
finished = True
if (episode % self.target_network_update) == 0:
# Update the target network with the latest policy network parameters
self.target_net.load_state_dict(self.policy_net.state_dict())
# Return the total rewards collected within this single episode run
return episode_rewards
def __init__(self, epsilon_decay=0.005, seed=1364):
# Epsilon parameters
self.current_epsilon = 0.99
self.epsilon_start = 0.99
self.epsilon_end = 0.05
self.epsilon_decay = epsilon_decay
# ----------------------------------------
# Make the algorithm outputs reproducible
make_deterministic(seed)
def __init__(self, seed=1364):
# Define the environment
self.env = gym.make('CartPole-v0').unwrapped
# ----------------------------------------
# Make the algorithm outputs reproducible
make_deterministic(seed, self.env)
# ----------------------------------------
self.env.reset()
# Get number of actions from gym action space
self.num_actions = self.env.action_space.n
# Get the space size
self.input_dim = self.env.state.size
self.score_required_to_win = 200
self.average_score_required_to_win = self.env.spec.reward_threshold
def __init__(self, seed=1364):
# Define the environment
self.env = gym.make('CartPole-v0').unwrapped
# ----------------------------------------
# Make the algorithm outputs reproducible
make_deterministic(seed, self.env)
# ----------------------------------------
self.env.reset()
# Get number of actions from gym action space
self.num_actions = self.env.action_space.n
# Get screen size so that we can initialize Q-network layers correctly based on shape
# returned from AI gym. Typical dimensions at this point are close to 3x40x90
# which is the result of a cropped and down-scaled render buffer in get_screen()
# the output of get_screen is a torch frame of shape (B, C, H, W)
_, _, screen_height, screen_width = self.get_screen().shape
self.input_dim = (screen_height, screen_width)
self.score_required_to_win = 200
self.average_score_required_to_win = self.env.spec.reward_threshold
def run_single_episode(self,
env,
episode,
number_of_learning_iterations_in_one_step=1):
# Make each episode deterministic based on the total_iteration_number
make_deterministic(self.total_steps_so_far, env.env)
finished = False
episode_rewards = []
episode_losses = []
# Create the first state of the episode
state_1 = env.get_state(episode_start=True)
while not finished:
env.env.render(mode='rgb_array')
action_1 = self.get_action(env, state_1)
# Take the selected action in the environment
s2, reward_1, finished, _ = env.env.step(action_1)
# when episode is finished, state_2 does not matter,
# and won't contribute to the optimisation
# (because state_1 was the last state of the episode)
state_2 = (0 * state_1) if finished else env.get_state()
# Add the current transition (s, a, r, s', done) to the replay memory
self.add_experience_to_replay_memory(state_1, action_1, reward_1,
state_2, finished)
# Policy Network optimisation:
# ----------------------------
# If there are enough sample transitions inside the replay_memory,
# then we can start training our policy network using them;
# Otherwise we move on to the next state of the episode.
if len(self.replay_memory) >= self.batch_size:
if self.total_steps_so_far % self.steps_between_learning_steps == 0:
for _ in range(number_of_learning_iterations_in_one_step):
# Take a random sample minibatch from the replay memory
minibatch = self.sample_from_replay_memory(
self.batch_size)
# Compute the TD loss over the minibatch
_, _ = self.learning_step(minibatch)
# Track the value of loss (for debugging purpose)
# episode_losses.append(loss.item())
# Update the target networks (polyac averaging)
self.soft_update_target_networks()
# Go to the next step of the episode
state_1 = state_2
# Add up the rewards collected during this episode
episode_rewards.append(reward_1)
# One single training iteration is passed
self.total_steps_so_far += 1
# If the agent has received a satisfactory episode reward, stop it.
if sum(episode_rewards) >= env.score_required_to_win:
finished = True
# If the episode takes longer than 'max_episode_length', terminate it.
if len(episode_rewards) > self.max_episode_length:
break
# print(f"episode: {episode}, reward: {reward_1}, action_1: {action_1}")
# Return the total rewards collected within this single episode run
return episode_rewards
def __init__(self,
input_dim,
action_dimension,
set_device=None,
gradient_clipping_norm=None,
learning_rate_actor=0.01,
learning_rate_critic=0.01,
actor_noise_scale=0.1,
steps_between_learning_steps=1,
max_episode_length=2000,
polyac=0.99,
seed=1364):
self.seed = seed
# Training parameters
self.gamma = 0.99
self.batch_size = 256
self.target_network_update = 10
self.total_steps_so_far = 0
self.max_episode_length = max_episode_length
self.save_model_frequency = 100
self.learning_rate_actor = learning_rate_actor
self.learning_rate_critic = learning_rate_critic
self.gradient_clipping_norm = gradient_clipping_norm
self.polyac = polyac
self.steps_between_learning_steps = steps_between_learning_steps
# Explorer
self.actor_noise_scale = actor_noise_scale
self.noise = OU_Noise(action_dimension, seed, 0, 0.15, 0.25)
self.noise.reset()
# Experience Replay Memory
self.memory_size = 1000000
self.replay_memory = deque([], maxlen=self.memory_size)
# ----------------------------------------
# Make the algorithm outputs reproducible
make_deterministic(seed)
# ----------------------------------------
# if gpu is to be used
if set_device is None:
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
# self.device = "cuda" if torch.cuda.is_available() else "cpu"
else:
self.device = torch.device(set_device)
# network instantiation
self.actor_net = ActorDDPG(input_dim, action_dimension).to(self.device)
self.critic_net = CriticDDPG(input_dim,
action_dimension).to(self.device)
self.actor_net_target = ActorDDPG(input_dim,
action_dimension).to(self.device)
self.critic_net_target = CriticDDPG(input_dim,
action_dimension).to(self.device)
self.actor_net_target.load_state_dict(self.actor_net.state_dict())
self.actor_net_target.eval()
self.critic_net_target.load_state_dict(self.critic_net.state_dict())
self.critic_net_target.eval()
# self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=self.learning_rate)
self.optimizer_actor = optim.Adam(self.actor_net.parameters(),
lr=self.learning_rate_actor,
eps=1e-4)
self.optimizer_critic = optim.Adam(self.critic_net.parameters(),
lr=self.learning_rate_critic,
eps=1e-4)
def __init__(self,
input_dim,
num_actions,
network_params=None,
explorer=None,
set_device=None,
gradient_clipping_norm=None,
learning_rate=0.01,
double_dqn=False,
seed=1364):
self.seed = seed
# Training parameters
self.gamma = 0.99
self.batch_size = 256
self.target_network_update = 10
self.total_steps_so_far = 0
self.save_model_frequency = 100
self.learning_rate = learning_rate
self.latest_learning_rate = learning_rate
self.gradient_clipping_norm = gradient_clipping_norm
# Explorer
if explorer is None:
self.explorer = ActionExplorer(epsilon_decay=0.005, seed=seed)
else:
self.explorer = explorer
# Experience Replay Memory
self.memory_size = 40000
self.replay_memory = deque([], maxlen=self.memory_size)
# ----------------------------------------
# Make the algorithm outputs reproducible
make_deterministic(seed)
# ----------------------------------------
# if gpu is to be used
if set_device is None:
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
# self.device = "cuda" if torch.cuda.is_available() else "cpu"
else:
self.device = torch.device(set_device)
# Alternative DQN training
self.double_dqn = double_dqn
# Q-network instantiation
# (Explanation of the network_params in networks/network_builder.py)
if network_params is None:
network_params = {
'input_dim': input_dim,
'conv_layers': [(3, 16, 5, 2), (16, 32, 5, 2), (32, 32, 5, 2)],
'dense_layers': [num_actions],
'conv_bn': True,
'activation': 'relu'
}
self.policy_net = CreateNet(network_params).to(self.device)
self.target_net = CreateNet(network_params).to(self.device)
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval()
# self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=self.learning_rate)
self.optimizer = optim.Adam(self.policy_net.parameters(),
lr=self.learning_rate,
eps=1e-4)