def info_sa_planning_experiment(min_grid_size=5, max_grid_size=11, beta=10.0):
'''
Args:
min_grid_size (int)
max_grid_size (int)
beta (float): Hyperparameter for InfoSA.
Summary:
Writes num iterations and time (seconds) for planning with and without abstractions.
'''
vanilla_file = "vi.csv"
sa_file = "vi-$\\phi$.csv"
file_prefix = os.path.join("results", "planning-four_room")
clear_files(dir_name=file_prefix)
for grid_dim in xrange(min_grid_size, max_grid_size + 1):
# ======================
# == Make Environment ==
# ======================
mdp = FourRoomMDP(width=grid_dim, height=grid_dim, init_loc=(1, 1), goal_locs=[(grid_dim, grid_dim)], gamma=0.9)
# Get demo policy.
vi = ValueIteration(mdp)
vi.run_vi()
demo_policy = get_lambda_policy(make_det_policy_eps_greedy(vi.policy, vi.get_states(), mdp.get_actions(), epsilon=0.2))
# =======================
# == Make Abstractions ==
# =======================
pmf_s_phi, phi_pmf, abstr_policy = run_info_sa(mdp, demo_policy, iters=500, beta=beta, convergence_threshold=0.00001)
lambda_abstr_policy = get_lambda_policy(abstr_policy)
prob_s_phi = ProbStateAbstraction(phi_pmf)
crisp_s_phi = convert_prob_sa_to_sa(prob_s_phi)
# ============
# == Run VI ==
# ============
vanilla_vi = ValueIteration(mdp, delta=0.0001, sample_rate=25)
sa_vi = AbstractValueIteration(ground_mdp=mdp, state_abstr=crisp_s_phi, delta=0.0001, vi_sample_rate=25, amdp_sample_rate=25)
# ==========
# == Plan ==
# ==========
print "Running VIs."
start_time = time.clock()
vanilla_iters, vanilla_val = vanilla_vi.run_vi()
vanilla_time = round(time.clock() - start_time, 2)
mdp.reset()
start_time = time.clock()
sa_iters, sa_abs_val = sa_vi.run_vi()
sa_time = round(time.clock() - start_time, 2)
sa_val = evaluate_agent(FixedPolicyAgent(sa_vi.policy), mdp, instances=25)
print "\n" + "*"*20
print "Vanilla", "\n\t Iters:", vanilla_iters, "\n\t Value:", round(vanilla_val, 4), "\n\t Time:", vanilla_time
print
print "Phi:", "\n\t Iters:", sa_iters, "\n\t Value:", round(sa_val, 4), "\n\t Time:", sa_time
print "*"*20 + "\n\n"
write_datum(os.path.join(file_prefix, "iters", vanilla_file), vanilla_iters)
write_datum(os.path.join(file_prefix, "iters", sa_file), sa_iters)
write_datum(os.path.join(file_prefix, "times", vanilla_file), vanilla_time)
write_datum(os.path.join(file_prefix, "times", sa_file), sa_time)
def branching_factor_experiment(min_options=0,
max_options=20,
increment=2,
instances=5,
epsilon=0.05):
'''
Args:
min_options (int)
max_options (int)
increment (int)
Summary:
Runs an experiment contrasting learning performance for different # options.
'''
# Define MDP.
grid_size = 7
mdp = FourRoomMDP(width=grid_size,
height=grid_size,
goal_locs=[(grid_size, grid_size)])
# Make State Abstraction.
states, _ = ah.compute_reachable_state_space(mdp, sample_rate=50)
state_abstr = core.compute_phi_given_m(mdp,
four_rooms_predicate_9x9,
level=1,
states=states)
x_axis = range(min_options, max_options + 1, increment)
y_axis = defaultdict(list) #[] #[0] * len(x_axis)
conf_intervals = defaultdict(list)
num_options_performance = defaultdict(lambda: defaultdict(list))
# Choose dependent variable (either #steps per episode or #episodes).
d_var_range = [(20, 5), (40, 250), (400, 2500)]
for steps, episodes in d_var_range:
print "steps, episodes", steps, episodes
# Evaluate.
for i, instance in enumerate(range(instances)):
print "\tInstance", instance + 1, "of", str(instances) + "."
# Make initial Options.
for num_options in x_axis:
options, _ = make_near_optimal_phi_relative_options(
mdp,
state_abstr,
'eps-greedy',
num_rand_opts=num_options - 1,
eps=epsilon)
action_abstr = ActionAbstraction(
options=options, prim_actions=mdp.get_actions())
# Make agent.
AgentClass = RMaxAgent # DoubleQAgent, QLearningAgent, SarsaAgent
sa_aa_agent = AbstractionWrapper(
AgentClass,
agent_params={"actions": mdp.get_actions()},
state_abstr=state_abstr,
action_abstr=action_abstr,
name_ext="-$\\phi,O$")
_, _, value_per_episode = run_single_agent_on_mdp(
sa_aa_agent, mdp, episodes=episodes, steps=steps)
mdp.reset()
num_options_performance[(steps, episodes)][num_options].append(
value_per_episode[-1])
############
# Other types
# Just state abstraction.
steps, episodes = d_var_range[-1][0], d_var_range[-1][1]
sa_agent = AbstractionWrapper(AgentClass,
agent_params={"actions": mdp.get_actions()},
state_abstr=state_abstr,
action_abstr=None,
name_ext="-$\\phi$")
_, _, value_per_episode = run_single_agent_on_mdp(sa_agent,
mdp,
episodes=episodes,
steps=steps)
num_options_performance[(d_var_range[-1][0],
d_var_range[-1][1])]["phi"].append(
value_per_episode[-1])
y_axis["phi"] = [value_per_episode[-1]]
# Run random options.
options = make_fixed_random_options(mdp, state_abstr)
action_abstr = ActionAbstraction(options=options,
prim_actions=mdp.get_actions())
AgentClass = QLearningAgent
rand_opt_agent = AbstractionWrapper(
AgentClass,
agent_params={"actions": mdp.get_actions()},
state_abstr=state_abstr,
action_abstr=action_abstr,
name_ext="-$\\phi,O_{\text{random}}$")
_, _, value_per_episode = run_single_agent_on_mdp(rand_opt_agent,
mdp,
episodes=episodes,
steps=steps)
num_options_performance[(d_var_range[-1][0],
d_var_range[-1][1])]["random"].append(
value_per_episode[-1])
y_axis["random"] = [value_per_episode[-1]]
# Makeoptimal agent.
value_iter = ValueIteration(mdp)
value_iter.run_vi()
optimal_agent = FixedPolicyAgent(value_iter.policy)
_, _, value_per_episode = run_single_agent_on_mdp(optimal_agent,
mdp,
episodes=episodes,
steps=steps)
y_axis["optimal"] = [value_per_episode[-1]]
total_steps = d_var_range[0][0] * d_var_range[0][1]
# Confidence intervals.
for dependent_var in d_var_range:
for num_options in x_axis:
# Compute mean and standard error.
avg_for_n = float(
sum(num_options_performance[dependent_var]
[num_options])) / instances
std_deviation = np.std(
num_options_performance[dependent_var][num_options])
std_error = 1.96 * (std_deviation / math.sqrt(
len(num_options_performance[dependent_var][num_options])))
y_axis[dependent_var].append(avg_for_n)
conf_intervals[dependent_var].append(std_error)
plt.xlabel("$|O_\\phi|$")
plt.xlim([1, len(x_axis)])
plt.ylabel("$V^{\hat{\pi}_{O_\\phi}}(s_0)$")
plt.tight_layout() # Keeps the spacing nice.
# Add just state abstraction.
ep_val_del_q_phi = y_axis["phi"]
label = "$O_{\\phi}$" #" N=1e" + str(str(total_steps).count("0")) + "$"
plt.plot(x_axis, [ep_val_del_q_phi] * len(x_axis),
marker="+",
linestyle="--",
linewidth=1.0,
color=PLOT_COLORS[-1],
label=label)
# Add random options.
ep_val_del_q = y_axis["random"]
label = "$O_{random}$" #" N=1e" + str(str(total_steps).count("0")) + "$"
plt.plot(x_axis, [ep_val_del_q] * len(x_axis),
marker="x",
linestyle="--",
linewidth=1.0,
color=PLOT_COLORS[0]) #, label=label)
# Add optimal.
ep_val_optimal = y_axis["optimal"]
plt.plot(x_axis, [ep_val_optimal] * len(x_axis),
linestyle="-",
linewidth=1.0,
color=PLOT_COLORS[1]) #, label="$\\pi^*$")
for i, dependent_var in enumerate(d_var_range):
total_steps = dependent_var[0] * dependent_var[1]
label = "$O_{\\phi,Q_\\varepsilon^*}, N=1e" + str(
str(total_steps).count("0")) + "$"
plt.plot(x_axis,
y_axis[dependent_var],
marker="x",
color=PLOT_COLORS[i + 2],
linewidth=1.5,
label=label)
# Confidence intervals.
top = np.add(y_axis[dependent_var], conf_intervals[dependent_var])
bot = np.subtract(y_axis[dependent_var], conf_intervals[dependent_var])
plt.fill_between(x_axis,
top,
bot,
alpha=0.25,
color=PLOT_COLORS[i + 2])
plt.legend()
plt.savefig("branching_factor_results.pdf", format="pdf")
plt.cla()
plt.close()
