def experiment(n_epochs, n_episodes):
np.random.seed()
logger = Logger(COPDAC_Q.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + COPDAC_Q.__name__)
# MDP
n_steps = 5000
mdp = InvertedPendulum(horizon=n_steps)
# Agent
n_tilings = 10
alpha_theta = Parameter(5e-3 / n_tilings)
alpha_omega = Parameter(0.5 / n_tilings)
alpha_v = Parameter(0.5 / n_tilings)
tilings = Tiles.generate(n_tilings, [10, 10],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
mu = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
sigma = 1e-1 * np.eye(1)
policy = GaussianPolicy(mu, sigma)
agent = COPDAC_Q(mdp.info,
policy,
mu,
alpha_theta,
alpha_omega,
alpha_v,
value_function_features=phi,
policy_features=phi)
# Train
dataset_callback = CollectDataset()
visualization_callback = Display(agent._V, mu,
mdp.info.observation_space.low,
mdp.info.observation_space.high, phi, phi)
core = Core(agent, mdp, callbacks_fit=[dataset_callback])
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=n_episodes, n_steps_per_fit=1, render=False)
J = compute_J(dataset_callback.get(), gamma=1.0)
dataset_callback.clean()
visualization_callback()
logger.epoch_info(i + 1, R_mean=np.sum(J) / n_steps / n_episodes)
logger.info('Press a button to visualize the pendulum...')
input()
sigma = 1e-8 * np.eye(1)
policy.set_sigma(sigma)
core.evaluate(n_steps=n_steps, render=True)
def experiment(n_epochs, n_iterations, ep_per_run, save_states_to_disk):
np.random.seed()
logger = Logger('plot_and_norm_example', results_dir=None)
logger.strong_line()
logger.info('Plotting and normalization example')
# MDP
mdp = LQR.generate(dimensions=2, max_pos=10., max_action=5., episodic=True)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
sigma = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
sigma_weights = 2 * np.ones(sigma.weights_size)
sigma.set_weights(sigma_weights)
policy = StateStdGaussianPolicy(approximator, sigma)
# Agent
optimizer = AdaptiveOptimizer(eps=.01)
algorithm_params = dict(optimizer=optimizer)
agent = REINFORCE(mdp.info, policy, **algorithm_params)
# normalization callback
prepro = MinMaxPreprocessor(mdp_info=mdp.info)
# plotting callback
plotter = PlotDataset(mdp.info, obs_normalized=True)
# Train
core = Core(agent, mdp, callback_step=plotter, preprocessors=[prepro])
# training loop
for n in range(n_epochs):
core.learn(n_episodes=n_iterations * ep_per_run,
n_episodes_per_fit=ep_per_run)
dataset = core.evaluate(n_episodes=ep_per_run, render=False)
J = np.mean(compute_J(dataset, mdp.info.gamma))
logger.epoch_info(n + 1, J=J)
if save_states_to_disk:
# save normalization / plot states to disk path
logger.info('Saving plotting and normalization data')
os.makedirs("./logs/plot_and_norm", exist_ok=True)
prepro.save("./logs/plot_and_norm/preprocessor.msh")
plotter.save_state("./logs/plot_and_norm/plotting_state")
# load states from disk path
logger.info('Loading preprocessor and plotter')
prerpo = MinMaxPreprocessor.load(
"./logs/plot_and_norm/preprocessor.msh")
plotter.load_state("./logs/plot_and_norm/plotting_state")
def experiment(alg, params, n_epochs, n_episodes, n_ep_per_fit):
np.random.seed()
print('============ start experiment ============')
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = GraspEnv()
print('============ mdp ============')
# Policy
n_weights = 6
mu = np.array([-0.5, 0.0, 0.91, m.pi, 0, 0])
sigma = np.asarray([0.05, 0.05, 0.05, 0.1, 0.1,
0.1]) #np.asarray([0.15, 0.15, 0.15, 0.4, 0.4, 0.4])
policy = Own_policy()
dist = GaussianDiagonalDistribution(
mu, sigma) # TODO: is this distribution right? Yes.
agent = alg(mdp.info, dist, policy, **params)
# Train
dataset_callback = CollectDataset(
) # TODO: should we also collect the dataset? Just keep this.
core = Core(agent, mdp, callbacks_fit=[dataset_callback])
#core = Core(agent, mdp)
for i in range(n_epochs):
print('================ core learn ================')
core.learn(n_episodes=n_episodes, n_episodes_per_fit=n_ep_per_fit)
J = compute_J(dataset_callback.get(), gamma=mdp.info.gamma)
print('J:', J)
print('============================')
dataset_callback.clean() # Todo: learning curve? Done
p = dist.get_parameters()
print('p:', p)
mu_0.append(p[:n_weights][0])
mu_1.append(p[:n_weights][1])
mu_2.append(p[:n_weights][2])
mu_3.append(p[:n_weights][3])
mu_4.append(p[:n_weights][4])
mu_5.append(p[:n_weights][5])
current_avg_sigma = (p[n_weights:][0] + p[n_weights:][1] +
p[n_weights:][2] + p[n_weights:][3] +
p[n_weights:][4] + p[n_weights:][5]) / 6
avg_sigma.append(current_avg_sigma)
# record learning curve of cumulative rewards
logger.epoch_info(i + 1,
J=np.mean(J),
mu=p[:n_weights],
sigma=p[n_weights:])
list_J.append(np.mean(J))
def experiment(alg, env_id, horizon, gamma, n_epochs, n_steps, n_steps_per_fit,
n_step_test, alg_params, policy_params):
logger = Logger(A2C.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + A2C.__name__)
mdp = Gym(env_id, horizon, gamma)
critic_params = dict(network=Network,
optimizer={
'class': optim.RMSprop,
'params': {
'lr': 7e-4,
'eps': 1e-5
}
},
loss=F.mse_loss,
n_features=64,
batch_size=64,
input_shape=mdp.info.observation_space.shape,
output_shape=(1, ))
alg_params['critic_params'] = critic_params
policy = GaussianTorchPolicy(Network, mdp.info.observation_space.shape,
mdp.info.action_space.shape, **policy_params)
agent = alg(mdp.info, policy, **alg_params)
core = Core(agent, mdp)
dataset = core.evaluate(n_steps=n_step_test, render=False)
J = np.mean(compute_J(dataset, mdp.info.gamma))
R = np.mean(compute_J(dataset))
E = agent.policy.entropy()
logger.epoch_info(0, J=J, R=R, entropy=E)
for it in trange(n_epochs):
core.learn(n_steps=n_steps, n_steps_per_fit=n_steps_per_fit)
dataset = core.evaluate(n_steps=n_step_test, render=False)
J = np.mean(compute_J(dataset, mdp.info.gamma))
R = np.mean(compute_J(dataset))
E = agent.policy.entropy()
logger.epoch_info(it + 1, J=J, R=R, entropy=E)
logger.info('Press a button to visualize')
input()
core.evaluate(n_episodes=5, render=True)
def experiment(alg, params, n_epochs, fit_per_epoch, ep_per_fit):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = ShipSteering()
# Policy
high = [150, 150, np.pi]
low = [0, 0, -np.pi]
n_tiles = [5, 5, 6]
low = np.array(low, dtype=np.float)
high = np.array(high, dtype=np.float)
n_tilings = 1
tilings = Tiles.generate(n_tilings=n_tilings,
n_tiles=n_tiles,
low=low,
high=high)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(approximator)
mu = np.zeros(policy.weights_size)
sigma = 4e-1 * np.ones(policy.weights_size)
distribution = GaussianDiagonalDistribution(mu, sigma)
# Agent
agent = alg(mdp.info, distribution, policy, features=phi, **params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(0, J=np.mean(J))
for i in range(n_epochs):
core.learn(n_episodes=fit_per_epoch * ep_per_fit,
n_episodes_per_fit=ep_per_fit)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(i + 1, J=np.mean(J))
def experiment(alg, params, n_epochs, n_episodes, n_ep_per_fit):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = Segway()
# Policy
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
n_weights = approximator.weights_size
mu = np.zeros(n_weights)
sigma = 2e-0 * np.ones(n_weights)
policy = DeterministicPolicy(approximator)
dist = GaussianDiagonalDistribution(mu, sigma)
agent = alg(mdp.info, dist, policy, **params)
# Train
dataset_callback = CollectDataset()
core = Core(agent, mdp, callbacks_fit=[dataset_callback])
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=n_episodes,
n_episodes_per_fit=n_ep_per_fit,
render=False)
J = compute_J(dataset_callback.get(), gamma=mdp.info.gamma)
dataset_callback.clean()
p = dist.get_parameters()
logger.epoch_info(i + 1,
J=np.mean(J),
mu=p[:n_weights],
sigma=p[n_weights:])
logger.info('Press a button to visualize the segway...')
input()
core.evaluate(n_episodes=3, render=True)
def experiment(alg, n_epochs, n_iterations, ep_per_run):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = LQR.generate(dimensions=2, max_action=1., max_pos=1.)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
sigma = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
sigma_weights = 0.25 * np.ones(sigma.weights_size)
sigma.set_weights(sigma_weights)
policy = StateStdGaussianPolicy(approximator, sigma)
# Agent
optimizer = AdaptiveOptimizer(eps=1e-2)
algorithm_params = dict(optimizer=optimizer)
agent = alg(mdp.info, policy, **algorithm_params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(0,
J=np.mean(J),
policy_weights=policy.get_weights().tolist())
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=n_iterations * ep_per_run,
n_episodes_per_fit=ep_per_run)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(i + 1,
J=np.mean(J),
policy_weights=policy.get_weights().tolist())
def experiment(goal, use_muscles, n_epochs, n_steps, n_episodes_test):
np.random.seed(1)
logger = Logger('SAC', results_dir=None)
logger.strong_line()
logger.info('Humanoid Experiment, Algorithm: SAC')
# MDP
gamma = 0.99
horizon = 2000
mdp = create_mdp(gamma, horizon, goal, use_muscles=use_muscles)
# Agent
agent = create_SAC_agent(mdp)
# normalization callback
normalizer = MinMaxPreprocessor(mdp_info=mdp.info)
# plotting callback
plotter = PlotDataset(mdp.info)
# Algorithm(with normalization and plotting)
core = Core(agent, mdp, callback_step=plotter, preprocessors=[normalizer])
dataset = core.evaluate(n_episodes=n_episodes_test, render=True)
J = np.mean(compute_J(dataset, gamma))
L = int(np.round(np.mean(episodes_length(dataset))))
logger.epoch_info(0, J=J, episode_lenght=L)
# training loop
for n in trange(n_epochs, leave=False):
core.learn(n_steps=n_steps, n_steps_per_fit=1)
dataset = core.evaluate(n_episodes=n_episodes_test, render=True)
J = np.mean(compute_J(dataset, gamma))
L = int(np.round(np.mean(episodes_length(dataset))))
logger.epoch_info(n+1, J=J, episode_lenght=L)
logger.info('Press a button to visualize humanoid')
input()
core.evaluate(n_episodes=10, render=True)
def experiment(alg, params, n_epochs, fit_per_epoch, ep_per_fit):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = LQR.generate(dimensions=1)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(mu=approximator)
mu = np.zeros(policy.weights_size)
sigma = 1e-3 * np.eye(policy.weights_size)
distribution = GaussianCholeskyDistribution(mu, sigma)
# Agent
agent = alg(mdp.info, distribution, policy, **params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(0,
J=np.mean(J),
distribution_parameters=distribution.get_parameters())
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=fit_per_epoch * ep_per_fit,
n_episodes_per_fit=ep_per_fit)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(
i + 1,
J=np.mean(J),
distribution_parameters=distribution.get_parameters())
def experiment(alg, n_epochs, n_steps, n_episodes_test):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
gamma = 0.99
habitat_root_path = Habitat.root_path()
config_file = os.path.join(
habitat_root_path, 'habitat_baselines/config/rearrange/rl_pick.yaml')
base_config_file = os.path.join(habitat_root_path,
'configs/tasks/rearrange/pick.yaml')
wrapper = 'HabitatRearrangeWrapper'
mdp = Habitat(wrapper, config_file, base_config_file, gamma=gamma)
# Settings
initial_replay_size = 64
max_replay_size = 50000
batch_size = 64
n_features = 64
warmup_transitions = 100
tau = 0.005
lr_alpha = 3e-4
use_cuda = torch.cuda.is_available()
# Approximator
actor_input_shape = mdp.info.observation_space.shape
actor_mu_params = dict(network=ActorNetwork,
n_features=n_features,
input_shape=actor_input_shape,
output_shape=mdp.info.action_space.shape,
use_cuda=use_cuda)
actor_sigma_params = dict(network=ActorNetwork,
n_features=n_features,
input_shape=actor_input_shape,
output_shape=mdp.info.action_space.shape,
use_cuda=use_cuda)
actor_optimizer = {'class': optim.Adam, 'params': {'lr': 3e-4}}
critic_input_shape = actor_input_shape + mdp.info.action_space.shape
critic_params = dict(network=CriticNetwork,
optimizer={
'class': optim.Adam,
'params': {
'lr': 3e-4
}
},
loss=F.mse_loss,
n_features=n_features,
input_shape=critic_input_shape,
output_shape=(1, ),
use_cuda=use_cuda)
# Agent
agent = alg(mdp.info,
actor_mu_params,
actor_sigma_params,
actor_optimizer,
critic_params,
batch_size,
initial_replay_size,
max_replay_size,
warmup_transitions,
tau,
lr_alpha,
critic_fit_params=None)
# Algorithm
core = Core(agent, mdp)
# RUN
dataset = core.evaluate(n_episodes=n_episodes_test, render=False)
s, *_ = parse_dataset(dataset)
J = np.mean(compute_J(dataset, mdp.info.gamma))
R = np.mean(compute_J(dataset))
E = agent.policy.entropy(s)
logger.epoch_info(0, J=J, R=R, entropy=E)
core.learn(n_steps=initial_replay_size,
n_steps_per_fit=initial_replay_size)
for n in trange(n_epochs, leave=False):
core.learn(n_steps=n_steps, n_steps_per_fit=1)
dataset = core.evaluate(n_episodes=n_episodes_test, render=False)
s, *_ = parse_dataset(dataset)
J = np.mean(compute_J(dataset, mdp.info.gamma))
R = np.mean(compute_J(dataset))
E = agent.policy.entropy(s)
logger.epoch_info(n + 1, J=J, R=R, entropy=E)
logger.info('Press a button to visualize the robot')
input()
core.evaluate(n_episodes=5, render=True)
def experiment(alg, n_epochs, n_steps, n_steps_test):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
use_cuda = torch.cuda.is_available()
# MDP
horizon = 200
gamma = 0.99
mdp = Gym('Pendulum-v1', horizon, gamma)
# Policy
policy_class = OrnsteinUhlenbeckPolicy
policy_params = dict(sigma=np.ones(1) * .2, theta=.15, dt=1e-2)
# Settings
initial_replay_size = 500
max_replay_size = 5000
batch_size = 200
n_features = 80
tau = .001
# Approximator
actor_input_shape = mdp.info.observation_space.shape
actor_params = dict(network=ActorNetwork,
n_features=n_features,
input_shape=actor_input_shape,
output_shape=mdp.info.action_space.shape,
use_cuda=use_cuda)
actor_optimizer = {'class': optim.Adam,
'params': {'lr': .001}}
critic_input_shape = (actor_input_shape[0] + mdp.info.action_space.shape[0],)
critic_params = dict(network=CriticNetwork,
optimizer={'class': optim.Adam,
'params': {'lr': .001}},
loss=F.mse_loss,
n_features=n_features,
input_shape=critic_input_shape,
output_shape=(1,),
use_cuda=use_cuda)
# Agent
agent = alg(mdp.info, policy_class, policy_params,
actor_params, actor_optimizer, critic_params, batch_size,
initial_replay_size, max_replay_size, tau)
# Algorithm
core = Core(agent, mdp)
core.learn(n_steps=initial_replay_size, n_steps_per_fit=initial_replay_size)
# RUN
dataset = core.evaluate(n_steps=n_steps_test, render=False)
J = np.mean(compute_J(dataset, gamma))
R = np.mean(compute_J(dataset))
logger.epoch_info(0, J=J, R=R)
for n in trange(n_epochs, leave=False):
core.learn(n_steps=n_steps, n_steps_per_fit=1)
dataset = core.evaluate(n_steps=n_steps_test, render=False)
J = np.mean(compute_J(dataset, gamma))
R = np.mean(compute_J(dataset))
logger.epoch_info(n+1, J=J, R=R)
logger.info('Press a button to visualize pendulum')
input()
core.evaluate(n_episodes=5, render=True)
mdp = generate_simple_chain(state_n=5, goal_states=[2], prob=.8, rew=1, gamma=.9)
epsilon = Parameter(value=.15)
pi = EpsGreedy(epsilon=epsilon)
agent = QLearning(mdp.info, pi, learning_rate=Parameter(value=.2))
core = Core(agent, mdp)
epochs = 10
# Initial policy Evaluation
logger.info('Experiment started')
logger.strong_line()
dataset = core.evaluate(n_steps=100)
J = np.mean(compute_J(dataset, mdp.info.gamma)) # Discounted returns
R = np.mean(compute_J(dataset)) # Undiscounted returns
logger.epoch_info(0, J=J, R=R, any_label='any value')
for i in trange(epochs):
# Here some learning
core.learn(n_steps=100, n_steps_per_fit=1)
sleep(0.5)
dataset = core.evaluate(n_steps=100)
sleep(0.5)
J = np.mean(compute_J(dataset, mdp.info.gamma)) # Discounted returns
R = np.mean(compute_J(dataset)) # Undiscounted returns
# Here logging epoch results to the console
logger.epoch_info(i+1, J=J, R=R)
# Logging the data in J.npy and E.npy
logger.log_numpy(J=J, R=R)
def experiment(alg, n_epochs, n_steps, n_steps_test):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
horizon = 200
gamma = 0.99
mdp = Gym('Pendulum-v1', horizon, gamma)
# Settings
initial_replay_size = 64
max_replay_size = 50000
batch_size = 64
n_features = 64
warmup_transitions = 100
tau = 0.005
lr_alpha = 3e-4
use_cuda = torch.cuda.is_available()
# Approximator
actor_input_shape = mdp.info.observation_space.shape
actor_mu_params = dict(network=ActorNetwork,
n_features=n_features,
input_shape=actor_input_shape,
output_shape=mdp.info.action_space.shape,
use_cuda=use_cuda)
actor_sigma_params = dict(network=ActorNetwork,
n_features=n_features,
input_shape=actor_input_shape,
output_shape=mdp.info.action_space.shape,
use_cuda=use_cuda)
actor_optimizer = {'class': optim.Adam,
'params': {'lr': 3e-4}}
critic_input_shape = (actor_input_shape[0] + mdp.info.action_space.shape[0],)
critic_params = dict(network=CriticNetwork,
optimizer={'class': optim.Adam,
'params': {'lr': 3e-4}},
loss=F.mse_loss,
n_features=n_features,
input_shape=critic_input_shape,
output_shape=(1,),
use_cuda=use_cuda)
# Agent
agent = alg(mdp.info, actor_mu_params, actor_sigma_params,
actor_optimizer, critic_params, batch_size, initial_replay_size,
max_replay_size, warmup_transitions, tau, lr_alpha,
critic_fit_params=None)
# Algorithm
core = Core(agent, mdp)
# RUN
dataset = core.evaluate(n_steps=n_steps_test, render=False)
s, *_ = parse_dataset(dataset)
J = np.mean(compute_J(dataset, mdp.info.gamma))
R = np.mean(compute_J(dataset))
E = agent.policy.entropy(s)
logger.epoch_info(0, J=J, R=R, entropy=E)
core.learn(n_steps=initial_replay_size, n_steps_per_fit=initial_replay_size)
for n in trange(n_epochs, leave=False):
core.learn(n_steps=n_steps, n_steps_per_fit=1)
dataset = core.evaluate(n_steps=n_steps_test, render=False)
s, *_ = parse_dataset(dataset)
J = np.mean(compute_J(dataset, mdp.info.gamma))
R = np.mean(compute_J(dataset))
E = agent.policy.entropy(s)
logger.epoch_info(n+1, J=J, R=R, entropy=E)
logger.info('Press a button to visualize pendulum')
input()
core.evaluate(n_episodes=5, render=True)
def experiment(n_epochs, n_episodes):
np.random.seed()
logger = Logger(StochasticAC_AVG.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + StochasticAC_AVG.__name__)
# MDP
n_steps = 5000
mdp = InvertedPendulum(horizon=n_steps)
# Agent
n_tilings = 11
alpha_r = Parameter(.0001)
alpha_theta = Parameter(.001 / n_tilings)
alpha_v = Parameter(.1 / n_tilings)
tilings = Tiles.generate(n_tilings - 1, [10, 10],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
tilings_v = tilings + Tiles.generate(
1, [1, 1], mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
psi = Features(tilings=tilings_v)
input_shape = (phi.size, )
mu = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std_0 = np.sqrt(1.)
std.set_weights(np.log(std_0) / n_tilings * np.ones(std.weights_size))
policy = StateLogStdGaussianPolicy(mu, std)
agent = StochasticAC_AVG(mdp.info,
policy,
alpha_theta,
alpha_v,
alpha_r,
lambda_par=.5,
value_function_features=psi,
policy_features=phi)
# Train
dataset_callback = CollectDataset()
display_callback = Display(agent._V, mu, std,
mdp.info.observation_space.low,
mdp.info.observation_space.high, phi, psi)
core = Core(agent, mdp, callbacks_fit=[dataset_callback])
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=n_episodes, n_steps_per_fit=1, render=False)
J = compute_J(dataset_callback.get(), gamma=1.)
dataset_callback.clean()
display_callback()
logger.epoch_info(i + 1, R_mean=np.sum(J) / n_steps / n_episodes)
logger.info('Press a button to visualize the pendulum...')
input()
core.evaluate(n_steps=n_steps, render=True)
def experiment(n_epochs, n_steps, n_steps_test):
np.random.seed()
logger = Logger(DQN.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + DQN.__name__)
# MDP
horizon = 1000
gamma = 0.99
gamma_eval = 1.
mdp = Gym('Acrobot-v1', horizon, gamma)
# Policy
epsilon = LinearParameter(value=1., threshold_value=.01, n=5000)
epsilon_test = Parameter(value=0.)
epsilon_random = Parameter(value=1.)
pi = EpsGreedy(epsilon=epsilon_random)
# Settings
initial_replay_size = 500
max_replay_size = 5000
target_update_frequency = 100
batch_size = 200
n_features = 80
train_frequency = 1
# Approximator
input_shape = mdp.info.observation_space.shape
approximator_params = dict(network=Network,
optimizer={
'class': optim.Adam,
'params': {
'lr': .001
}
},
loss=F.smooth_l1_loss,
n_features=n_features,
input_shape=input_shape,
output_shape=mdp.info.action_space.size,
n_actions=mdp.info.action_space.n)
# Agent
agent = DQN(mdp.info,
pi,
TorchApproximator,
approximator_params=approximator_params,
batch_size=batch_size,
initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size,
target_update_frequency=target_update_frequency)
# Algorithm
core = Core(agent, mdp)
core.learn(n_steps=initial_replay_size,
n_steps_per_fit=initial_replay_size)
# RUN
pi.set_epsilon(epsilon_test)
dataset = core.evaluate(n_steps=n_steps_test, render=False)
J = compute_J(dataset, gamma_eval)
logger.epoch_info(0, J=np.mean(J))
for n in trange(n_epochs):
pi.set_epsilon(epsilon)
core.learn(n_steps=n_steps, n_steps_per_fit=train_frequency)
pi.set_epsilon(epsilon_test)
dataset = core.evaluate(n_steps=n_steps_test, render=False)
J = compute_J(dataset, gamma_eval)
logger.epoch_info(n + 1, J=np.mean(J))
logger.info('Press a button to visualize acrobot')
input()
core.evaluate(n_episodes=5, render=True)
def experiment(n_epochs, n_steps, n_steps_per_fit, n_step_test):
np.random.seed()
logger = Logger(A2C.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + A2C.__name__)
# MDP
horizon = 1000
gamma = 0.99
gamma_eval = 1.
mdp = Gym('Acrobot-v1', horizon, gamma)
# Policy
policy_params = dict(
n_features=32,
use_cuda=False
)
beta = Parameter(1e0)
pi = BoltzmannTorchPolicy(Network,
mdp.info.observation_space.shape,
(mdp.info.action_space.n,),
beta=beta,
**policy_params)
# Agent
critic_params = dict(network=Network,
optimizer={'class': optim.RMSprop,
'params': {'lr': 1e-3,
'eps': 1e-5}},
loss=F.mse_loss,
n_features=32,
batch_size=64,
input_shape=mdp.info.observation_space.shape,
output_shape=(1,))
alg_params = dict(actor_optimizer={'class': optim.RMSprop,
'params': {'lr': 1e-3,
'eps': 3e-3}},
critic_params=critic_params,
ent_coeff=0.01
)
agent = A2C(mdp.info, pi, **alg_params)
# Algorithm
core = Core(agent, mdp)
core.learn(n_steps=n_steps, n_steps_per_fit=n_steps_per_fit)
# RUN
dataset = core.evaluate(n_steps=n_step_test, render=False)
J = compute_J(dataset, gamma_eval)
logger.epoch_info(0, J=np.mean(J))
for n in trange(n_epochs):
core.learn(n_steps=n_steps, n_steps_per_fit=n_steps_per_fit)
dataset = core.evaluate(n_steps=n_step_test, render=False)
J = compute_J(dataset, gamma_eval)
logger.epoch_info(n+1, J=np.mean(J))
logger.info('Press a button to visualize acrobot')
input()
core.evaluate(n_episodes=5, render=True)
