def server_experiment_small(alg_high, alg_low, params, subdir, i):
np.random.seed()
# Model Block
mdp = ShipSteering(small=False, n_steps_action=3)
#State Placeholder
state_ph = PlaceHolder(name='state_ph')
#Reward Placeholder
reward_ph = PlaceHolder(name='reward_ph')
#Last_In Placeholder
lastaction_ph = PlaceHolder(name='lastaction_ph')
# Function Block 1
function_block1 = fBlock(name='f1 (angle difference)',
phi=pos_ref_angle_difference)
# Function Block 2
function_block2 = fBlock(name='f2 (cost cosine)', phi=cost_cosine)
#Features
features = Features(basis_list=[PolynomialBasis()])
# Policy 1
sigma1 = np.array([255, 255])
approximator1 = Regressor(LinearApproximator,
input_shape=(features.size, ),
output_shape=(2, ))
approximator1.set_weights(np.array([500, 500]))
pi1 = DiagonalGaussianPolicy(mu=approximator1, std=sigma1)
# Policy 2
pi2 = DeterministicControlPolicy(weights=np.array([0]))
mu2 = np.zeros(pi2.weights_size)
sigma2 = 1e-3 * np.ones(pi2.weights_size)
distribution2 = GaussianDiagonalDistribution(mu2, sigma2)
# Agent 1
learning_rate1 = params.get('learning_rate_high')
lim = 1000
mdp_info_agent1 = MDPInfo(observation_space=mdp.info.observation_space,
action_space=spaces.Box(0, lim, (2, )),
gamma=mdp.info.gamma,
horizon=100)
agent1 = alg_high(policy=pi1,
mdp_info=mdp_info_agent1,
learning_rate=learning_rate1,
features=features)
# Agent 2
learning_rate2 = params.get('learning_rate_low')
mdp_info_agent2 = MDPInfo(observation_space=spaces.Box(
-np.pi, np.pi, (1, )),
action_space=mdp.info.action_space,
gamma=mdp.info.gamma,
horizon=100)
agent2 = alg_low(distribution=distribution2,
policy=pi2,
mdp_info=mdp_info_agent2,
learning_rate=learning_rate2)
# Control Block 1
parameter_callback1 = CollectPolicyParameter(pi1)
control_block1 = ControlBlock(name='Control Block 1',
agent=agent1,
n_eps_per_fit=ep_per_run,
callbacks=[parameter_callback1])
# Control Block 2
parameter_callback2 = CollectDistributionParameter(distribution2)
control_block2 = ControlBlock(name='Control Block 2',
agent=agent2,
n_eps_per_fit=10,
callbacks=[parameter_callback2])
#Reward Accumulator
reward_acc = reward_accumulator_block(gamma=mdp_info_agent1.gamma,
name='reward_acc')
# Algorithm
blocks = [
state_ph, reward_ph, lastaction_ph, control_block1, control_block2,
function_block1, function_block2, reward_acc
]
state_ph.add_input(control_block2)
reward_ph.add_input(control_block2)
lastaction_ph.add_input(control_block2)
control_block1.add_input(state_ph)
reward_acc.add_input(reward_ph)
reward_acc.add_alarm_connection(control_block2)
control_block1.add_reward(reward_acc)
control_block1.add_alarm_connection(control_block2)
function_block1.add_input(control_block1)
function_block1.add_input(state_ph)
function_block2.add_input(function_block1)
control_block2.add_input(function_block1)
control_block2.add_reward(function_block2)
computational_graph = ComputationalGraph(blocks=blocks, model=mdp)
core = HierarchicalCore(computational_graph)
# Train
low_level_dataset_eval = list()
dataset_eval = list()
dataset_eval_run = core.evaluate(n_episodes=eval_run)
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
dataset_eval += dataset_eval_run
for n in range(n_runs):
print('ITERATION', n)
core.learn(n_episodes=n_iterations * ep_per_run, skip=True)
dataset_eval_run = core.evaluate(n_episodes=eval_run)
dataset_eval += dataset_eval_run
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at iteration ' + str(n) + ': ' + str(np.mean(J)))
low_level_dataset_eval += control_block2.dataset.get()
# Save
parameter_dataset1 = parameter_callback1.get_values()
parameter_dataset2 = parameter_callback2.get_values()
mk_dir_recursive('./' + subdir + str(i))
np.save(subdir + str(i) + '/low_level_dataset_file',
low_level_dataset_eval)
np.save(subdir + str(i) + '/parameter_dataset1_file', parameter_dataset1)
np.save(subdir + str(i) + '/parameter_dataset2_file', parameter_dataset2)
np.save(subdir + str(i) + '/dataset_eval_file', dataset_eval)
def build_computational_graph(mdp, agent_low, agent_high, ep_per_fit_low,
ep_per_fit_high):
# State Placeholder
state_ph = PlaceHolder(name='state_ph')
# Reward Placeholder
reward_ph = PlaceHolder(name='reward_ph')
# Last_In Placeholder
lastaction_ph = PlaceHolder(name='lastaction_ph')
# Function Block 1
function_block1 = fBlock(name='f1 (pick distance to goal state var)',
phi=pick_first_state)
# Function Block 2
function_block2 = fBlock(name='f2 (build state)',
phi=angle_to_angle_diff_complete_state)
# Function Block 3
function_block3 = fBlock(name='f3 (reward low level)', phi=lqr_cost_segway)
# Function Block 4
function_block4 = addBlock(name='f4 (add block)')
# Function Block 5
function_block5 = fBlock(name='f5 (fall punish low level)',
phi=fall_reward)
# Control Block 1
parameter_callback1 = CollectDistributionParameter(agent_high.distribution)
control_block_h = ControlBlock(name='Control Block High',
agent=agent_high,
n_eps_per_fit=ep_per_fit_high,
callbacks=[parameter_callback1])
# Control Block 2
parameter_callback2 = CollectDistributionParameter(agent_low.distribution)
control_block_l = ControlBlock(name='Control Block Low',
agent=agent_low,
n_eps_per_fit=ep_per_fit_low,
callbacks=[parameter_callback2])
control_block_h.set_mask()
# Graph
blocks = [
state_ph, reward_ph, lastaction_ph, control_block_h, control_block_l,
function_block1, function_block2, function_block3, function_block4,
function_block5
]
state_ph.add_input(control_block_l)
reward_ph.add_input(control_block_l)
lastaction_ph.add_input(control_block_l)
control_block_h.add_input(function_block1)
control_block_h.add_reward(reward_ph)
control_block_l.add_input(function_block2)
control_block_l.add_reward(function_block4)
function_block1.add_input(state_ph)
function_block2.add_input(control_block_h)
function_block2.add_input(state_ph)
function_block3.add_input(function_block2)
function_block5.add_input(state_ph)
function_block4.add_input(function_block3)
function_block4.add_input(function_block5)
computational_graph = ComputationalGraph(blocks=blocks, model=mdp)
return computational_graph, control_block_h
def segway_experiment(alg_high, alg_low, params_high, params_low):
np.random.seed()
# Model Block
mdp = SegwayLinearMotion(goal_distance=1.0)
#State Placeholder
state_ph = PlaceHolder(name='state_ph')
#Reward Placeholder
reward_ph = PlaceHolder(name='reward_ph')
#Last_In Placeholder
lastaction_ph = PlaceHolder(name='lastaction_ph')
# Function Block 1
function_block1 = fBlock(name='f1 (pick distance to goal state var)',
phi=pick_first_state)
# Function Block 2
function_block2 = fBlock(name='f2 (build state)',
phi=angle_to_angle_diff_complete_state)
# Function Block 3
function_block3 = fBlock(name='f3 (reward low level)',
phi=lqr_cost_segway)
# Function Block 4
function_block4 = addBlock(name='f4 (add block)')
# Function Block 5
function_block5 = fBlock(name='f5 (fall punish low level)', phi=fall_reward)
# Features
approximator1 = Regressor(LinearApproximator,
input_shape=(1,),
output_shape=(1,))
# Policy H
n_weights = approximator1.weights_size
mu1 = np.zeros(n_weights)
sigma1 = 2.0e-2*np.ones(n_weights)
pi1 = DeterministicPolicy(approximator1)
dist1 = GaussianDiagonalDistribution(mu1, sigma1)
# Agent H
lim = np.pi/2
mdp_info_agent1 = MDPInfo(observation_space=mdp.info.observation_space,
action_space=spaces.Box(-lim, lim, (1,)),
gamma=mdp.info.gamma,
horizon=mdp.info.horizon)
agent_high = alg_high(dist1, pi1, mdp_info_agent1, **params_high)
# Policy L
approximator2 = Regressor(LinearApproximator,
input_shape=(3,),
output_shape=(1,))
n_weights2 = approximator2.weights_size
mu2 = np.zeros(n_weights2)
sigma2 = 2.0*np.ones(n_weights2)
pi2 = DeterministicControlPolicy(approximator2)
dist2 = GaussianDiagonalDistribution(mu2, sigma2)
# Agent Low
mdp_info_agent2 = MDPInfo(observation_space=spaces.Box(
low=mdp.info.observation_space.low[1:], #FIXME FALSE
high=mdp.info.observation_space.high[1:], #FIXME FALSE
shape=(3,)),
action_space=mdp.info.action_space,
gamma=mdp.info.gamma, horizon=mdp.info.horizon)
agent_low = alg_low(dist2, pi2, mdp_info_agent2, **params_low)
# Control Block 1
parameter_callback1 = CollectDistributionParameter(dist1)
control_block1 = ControlBlock(name='Control Block High', agent=agent_high,
n_eps_per_fit=n_ep_per_fit*2,
callbacks=[parameter_callback1])
# Control Block 2
parameter_callback2 = CollectDistributionParameter(dist2)
control_block2 = ControlBlock(name='Control Block Low', agent=agent_low,
n_eps_per_fit=n_ep_per_fit,
callbacks=[parameter_callback2])
control_block1.set_mask()
# Algorithm
blocks = [state_ph, reward_ph, lastaction_ph, control_block1,
control_block2, function_block1, function_block2,
function_block3, function_block4, function_block5]
state_ph.add_input(control_block2)
reward_ph.add_input(control_block2)
lastaction_ph.add_input(control_block2)
control_block1.add_input(function_block1)
control_block1.add_reward(reward_ph)
control_block2.add_input(function_block2)
control_block2.add_reward(function_block4)
function_block1.add_input(state_ph)
function_block2.add_input(control_block1)
function_block2.add_input(state_ph)
function_block3.add_input(function_block2)
function_block5.add_input(state_ph)
function_block4.add_input(function_block3)
function_block4.add_input(function_block5)
computational_graph = ComputationalGraph(blocks=blocks, model=mdp)
core = HierarchicalCore(computational_graph)
# Train
dataset_eval_run = core.evaluate(n_episodes=eval_run, render=False)
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
mask_done = False
for n in range(n_epochs):
print('ITERATION', n)
if n == 2:
control_block1.unset_mask()
core.learn(n_episodes=n_iterations*n_ep_per_fit, skip=True)
dataset_eval_run = core.evaluate(n_episodes=eval_run, render=False)
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at iteration ' + str(n) + ': ' + str(np.mean(J)))
print('dist H:', dist1.get_parameters())
print('dist L mu:', dist2.get_parameters()[:3])
print('dist L sigma:', dist2.get_parameters()[3:])
def server_experiment_small(alg_high, alg_low, params, subdir, i):
np.random.seed()
# Model Block
mdp = SegwayLinearMotion(goal_distance=1.0)
#State Placeholder
state_ph = PlaceHolder(name='state_ph')
#Reward Placeholder
reward_ph = PlaceHolder(name='reward_ph')
#Last_In Placeholder
lastaction_ph = PlaceHolder(name='lastaction_ph')
# Function Block 1
function_block1 = fBlock(name='f1 (pick distance to goal state var)',
phi=pick_first_state)
# Function Block 2
function_block2 = fBlock(name='f2 (build state)',
phi=angle_to_angle_diff_complete_state)
# Function Block 3
function_block3 = fBlock(name='f3 (reward low level)', phi=lqr_cost_segway)
# Function Block 4
function_block4 = addBlock(name='f4 (add block)')
# Integrator Block
error_acc = ErrorAccumulatorBlock(name='error acc')
# Features
features1 = Features(basis_list=[PolynomialBasis()])
approximator1 = Regressor(LinearApproximator,
input_shape=(features1.size, ),
output_shape=(1, ))
# Policy 1
n_weights = approximator1.weights_size
mu1 = np.zeros(n_weights)
sigma1 = 2e-0 * np.ones(n_weights)
pi1 = DeterministicPolicy(approximator1)
dist1 = GaussianDiagonalDistribution(mu1, sigma1)
# Agent 1
eps1 = params.get('eps')
lim = 2 * np.pi
mdp_info_agent1 = MDPInfo(observation_space=mdp.info.observation_space,
action_space=spaces.Box(0, lim, (1, )),
gamma=mdp.info.gamma,
horizon=20)
agent1 = alg_low(distribution=dist1,
policy=pi1,
features=features1,
mdp_info=mdp_info_agent1,
eps=eps1)
# Policy 2
basis = PolynomialBasis.generate(1, 3)
features2 = Features(basis_list=basis)
approximator2 = Regressor(LinearApproximator,
input_shape=(features2.size, ),
output_shape=(1, ))
n_weights2 = approximator2.weights_size
mu2 = np.zeros(n_weights2)
sigma2 = 2e-0 * np.ones(n_weights2)
pi2 = DeterministicPolicy(approximator2)
dist2 = GaussianDiagonalDistribution(mu2, sigma2)
# Agent 2
mdp_info_agent2 = MDPInfo(observation_space=spaces.Box(
low=np.array([-np.pi, -np.pi, -np.pi]),
high=np.array([np.pi, np.pi, np.pi]),
shape=(3, )),
action_space=mdp.info.action_space,
gamma=mdp.info.gamma,
horizon=30)
agent2 = alg_low(distribution=dist2,
policy=pi2,
features=features2,
mdp_info=mdp_info_agent2,
eps=eps1)
# Control Block 1
parameter_callback1 = CollectDistributionParameter(dist1)
control_block1 = ControlBlock(name='Control Block 1',
agent=agent1,
n_eps_per_fit=ep_per_run,
callbacks=[parameter_callback1])
# Control Block 2
parameter_callback2 = CollectDistributionParameter(dist2)
control_block2 = ControlBlock(name='Control Block 2',
agent=agent2,
n_eps_per_fit=20,
callbacks=[parameter_callback2])
#Reward Accumulator
reward_acc = reward_accumulator_block(gamma=mdp_info_agent1.gamma,
name='reward_acc')
# Algorithm
blocks = [
state_ph, reward_ph, lastaction_ph, control_block1, control_block2,
function_block1, function_block2, function_block3, function_block4,
error_acc, reward_acc
]
state_ph.add_input(control_block2)
reward_ph.add_input(control_block2)
lastaction_ph.add_input(control_block2)
reward_acc.add_input(reward_ph)
reward_acc.add_alarm_connection(control_block2)
control_block1.add_input(function_block1)
control_block1.add_reward(reward_acc)
control_block1.add_alarm_connection(control_block2)
control_block2.add_input(function_block2)
control_block2.add_reward(function_block4)
function_block1.add_input(state_ph)
function_block2.add_input(control_block1)
function_block2.add_input(state_ph)
function_block3.add_input(function_block2)
error_acc.add_input(function_block3)
error_acc.add_alarm_connection(control_block2)
function_block4.add_input(function_block3)
function_block4.add_input(error_acc)
computational_graph = ComputationalGraph(blocks=blocks, model=mdp)
core = HierarchicalCore(computational_graph)
# Train
low_level_dataset_eval = list()
dataset_eval = list()
dataset_eval_run = core.evaluate(n_episodes=eval_run, render=True)
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
dataset_eval += dataset_eval_run
for n in range(n_runs):
print('ITERATION', n)
core.learn(n_episodes=n_iterations * ep_per_run, skip=True)
dataset_eval_run = core.evaluate(n_episodes=eval_run, render=True)
dataset_eval += dataset_eval_run
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at iteration ' + str(n) + ': ' + str(np.mean(J)))