def runOptimizationTask(task,
task_solver,
initial_distribution,
updater,
n_updates,
n_samples_per_update,
fig=None,
directory=None):
distribution = initial_distribution
all_costs = []
learning_curve = []
exploration_curve = []
if fig:
ax_space = fig.add_subplot(141)
ax_rollout = fig.add_subplot(142)
# Optimization loop
for i_update in range(n_updates):
# 0. Get cost of current distribution mean
cost_vars_eval = task_solver.performRollout(distribution.mean)
cost_eval = task.evaluateRollout(cost_vars_eval, distribution.mean)
rollout_eval = Rollout(distribution.mean, cost_vars_eval, cost_eval)
# 1. Sample from distribution
samples = distribution.generateSamples(n_samples_per_update)
# 2. Evaluate the samples
costs = np.full(n_samples_per_update, 0.0)
rollouts = []
for i_sample in range(n_samples_per_update):
# 2A. Perform the rollouts
cost_vars = task_solver.performRollout(samples[i_sample, :])
# 2B. Evaluate the rollouts
cur_costs = task.evaluateRollout(cost_vars, samples[i_sample, :])
costs[i_sample] = cur_costs[0]
rollouts.append(Rollout(samples[i_sample, :], cost_vars,
cur_costs))
# 3. Update parameters
distribution_new, weights = updater.updateDistribution(
distribution, samples, costs)
# Bookkeeping and plotting
# All the costs so far
all_costs.extend(costs)
# Update exploration curve
cur_samples = i_update * n_samples_per_update
cur_exploration = np.sqrt(distribution.maxEigenValue())
exploration_curve.append([cur_samples, cur_exploration])
# Update learning curve
learning_curve.append([cur_samples])
learning_curve[-1].extend(cost_eval)
# Plot summary of this update
if fig:
highlight = (i_update == 0)
plotUpdate(distribution, cost_eval, samples, costs, weights,
distribution_new, ax_space, highlight)
# Try plotting rollout with task solver
h = task_solver.plotRollout(rollout_eval.cost_vars, ax_rollout)
if not h:
# Task solver didn't know how to plot the rollout, try plotting
# it with task instead
h = task.plotRollout(rollout_eval.cost_vars, ax_rollout)
if h:
# If there is a handle, change its color depending on i_update
setColor(h, i_update, n_updates)
if directory:
saveUpdateRollouts(directory, i_update, distribution, rollout_eval,
rollouts, weights, distribution_new)
# Distribution is new distribution
distribution = distribution_new
# Plot learning curve
cost_labels = task.costLabels()
if fig:
plotExplorationCurve(exploration_curve, fig.add_subplot(143))
plotLearningCurve(learning_curve, fig.add_subplot(144), all_costs,
cost_labels)
# Save learning curve to file, if necessary
if directory:
saveLearningCurve(directory, learning_curve)
saveExplorationCurve(directory, exploration_curve)
print('Saved results to "' + directory + '".')
return learning_curve
def runOptimization(cost_function,
initial_distribution,
updater,
n_updates,
n_samples_per_update,
fig=None,
directory=None):
""" Run an evolutionary optimization process, see \ref page_bbo
\param[in] cost_function The cost function to optimize
\param[in] initial_distribution The initial parameter distribution
\param[in] updater The Updater used to update the parameters
\param[in] n_updates The number of updates to perform
\param[in] n_samples_per_update The number of samples per update
\param[in] fig Optional figure to plot the optimization.
\param[in] directory Optional directory to save to (default: don't save)
\return A learning curve that has the following format
#rows is number of optimization updates
column 0: Number of samples at which the cost was evaluated
column 1: The total cost
column 2...: Individual cost components (column 1 is their sum)
"""
distribution = initial_distribution
all_costs = []
learning_curve = []
exploration_curve = []
if fig:
ax = fig.add_subplot(131)
# Optimization loop
for i_update in range(n_updates):
# 0. Get cost of current distribution mean
cost_eval = cost_function.evaluate(distribution.mean)
# 1. Sample from distribution
samples = distribution.generateSamples(n_samples_per_update)
# 2. Evaluate the samples
costs = []
for i_sample in range(n_samples_per_update):
costs.append(cost_function.evaluate(samples[i_sample, :]))
# 3. Update parameters
distribution_new, weights = updater.updateDistribution(
distribution, samples, costs)
# Bookkeeping and plotting
# All the costs so far
all_costs.extend(costs)
# Update exploration curve
cur_samples = i_update * n_samples_per_update
cur_exploration = np.sqrt(distribution.maxEigenValue())
exploration_curve.append([cur_samples, cur_exploration])
# Update learning curve
learning_curve.append([cur_samples])
learning_curve[-1].extend(cost_eval)
# Plot summary of this update
if fig:
highlight = (i_update == 0)
plotUpdate(distribution, cost_eval, samples, costs, weights,
distribution_new, ax, highlight)
if directory:
saveUpdate(directory, i_update, distribution, cost_eval, samples,
costs, weights, distribution_new)
# Distribution is new distribution
distribution = distribution_new
# Plot learning curve
if fig:
plotExplorationCurve(exploration_curve, fig.add_subplot(132))
plotLearningCurve(learning_curve, fig.add_subplot(133), all_costs)
# Save learning curve to file, if necessary
if directory:
saveLearningCurve(directory, learning_curve)
saveExplorationCurve(directory, exploration_curve)
print('Saved results to "' + directory + '".')
return learning_curve
def runOptimizationTask(task, task_solver, initial_distribution, updater, n_updates, n_samples_per_update,fig=None,directory=None):
distribution = initial_distribution
all_costs = []
learning_curve = []
exploration_curve = []
if fig:
ax_space = fig.add_subplot(141)
ax_rollout = fig.add_subplot(142)
# Optimization loop
for i_update in range(n_updates):
# 0. Get cost of current distribution mean
cost_vars_eval = task_solver.performRollout(distribution.mean)
cost_eval = task.evaluateRollout(cost_vars_eval,distribution.mean)
rollout_eval = Rollout(distribution.mean,cost_vars_eval,cost_eval)
# 1. Sample from distribution
samples = distribution.generateSamples(n_samples_per_update)
# 2. Evaluate the samples
costs = np.full(n_samples_per_update,0.0)
rollouts = []
for i_sample in range(n_samples_per_update):
# 2A. Perform the rollouts
cost_vars = task_solver.performRollout(samples[i_sample,:])
# 2B. Evaluate the rollouts
cur_costs = task.evaluateRollout(cost_vars,samples[i_sample,:])
costs[i_sample] = cur_costs[0]
rollouts.append(Rollout(samples[i_sample,:],cost_vars,cur_costs))
# 3. Update parameters
distribution_new, weights = updater.updateDistribution(distribution, samples, costs)
# Bookkeeping and plotting
# All the costs so far
all_costs.extend(costs)
# Update exploration curve
cur_samples = i_update*n_samples_per_update
cur_exploration = np.sqrt(distribution.maxEigenValue())
exploration_curve.append([cur_samples,cur_exploration])
# Update learning curve
learning_curve.append([cur_samples])
learning_curve[-1].extend(cost_eval)
# Plot summary of this update
if fig:
highlight = (i_update==0)
plotUpdate(distribution,cost_eval,samples,costs,weights,distribution_new,ax_space,highlight)
# Try plotting rollout with task solver
h = task_solver.plotRollout(rollout_eval.cost_vars,ax_rollout)
if not h:
# Task solver didn't know how to plot the rollout, try plotting
# it with task instead
h = task.plotRollout(rollout_eval.cost_vars,ax_rollout)
if h:
# If there is a handle, change its color depending on i_update
setColor(h,i_update,n_updates)
if directory:
saveUpdateRollouts(directory, i_update, distribution, rollout_eval, rollouts, weights, distribution_new)
# Distribution is new distribution
distribution = distribution_new
# Plot learning curve
cost_labels = task.costLabels()
if fig:
plotExplorationCurve(exploration_curve,fig.add_subplot(143))
plotLearningCurve(learning_curve,fig.add_subplot(144),all_costs,cost_labels)
# Save learning curve to file, if necessary
if directory:
saveLearningCurve(directory,learning_curve)
saveExplorationCurve(directory,exploration_curve)
print('Saved results to "'+directory+'".')
return learning_curve
def runOptimization(cost_function,
initial_distribution,
updater,
n_updates,
n_samples_per_update,
fig=None,
directory=None):
distribution = initial_distribution
all_costs = []
learning_curve = []
exploration_curve = []
if fig:
ax = fig.add_subplot(131)
# Optimization loop
for i_update in range(n_updates):
# 0. Get cost of current distribution mean
cost_eval = cost_function.evaluate(distribution.mean)
# 1. Sample from distribution
samples = distribution.generateSamples(n_samples_per_update)
# 2. Evaluate the samples
costs = []
for i_sample in range(n_samples_per_update):
costs.append(cost_function.evaluate(samples[i_sample, :]))
# 3. Update parameters
distribution_new, weights = updater.updateDistribution(
distribution, samples, costs)
# Bookkeeping and plotting
# All the costs so far
all_costs.extend(costs)
# Update exploration curve
cur_samples = i_update * n_samples_per_update
cur_exploration = np.sqrt(distribution.maxEigenValue())
exploration_curve.append([cur_samples, cur_exploration])
# Update learning curve
learning_curve.append([cur_samples])
learning_curve[-1].extend(cost_eval)
# Plot summary of this update
if fig:
highlight = (i_update == 0)
plotUpdate(distribution, cost_eval, samples, costs, weights,
distribution_new, ax, highlight)
if directory:
saveUpdate(directory, i_update, distribution, cost_eval, samples,
costs, weights, distribution_new)
# Distribution is new distribution
distribution = distribution_new
# Plot learning curve
if fig:
plotExplorationCurve(exploration_curve, fig.add_subplot(132))
plotLearningCurve(learning_curve, fig.add_subplot(133), all_costs)
# Save learning curve to file, if necessary
if directory:
saveLearningCurve(directory, learning_curve)
saveExplorationCurve(directory, exploration_curve)
print('Saved results to "' + directory + '".')
return learning_curve