def main():
# Setup MDP, Agents.
mdp = FourRoomMDP(5, 5, goal_locs=[(5, 5)], gamma=0.99, step_cost=0.01)
# mdp = make_grid_world_from_file("octogrid.txt", num_goals=12, randomize=False)
ql_agent = QLearningAgent(mdp.get_actions(), epsilon=0.2, alpha=0.5)
rm_agent = RMaxAgent(mdp.get_actions())
viz = parse_args()
viz = "learning"
if viz == "value":
# Run experiment and make plot.
mdp.visualize_value()
elif viz == "policy":
# Viz policy
value_iter = ValueIteration(mdp)
value_iter.run_vi()
policy = value_iter.policy
mdp.visualize_policy(policy)
elif viz == "agent":
# Solve problem and show agent interaction.
print("\n", str(ql_agent), "interacting with", str(mdp))
run_single_agent_on_mdp(ql_agent, mdp, episodes=500, steps=200)
mdp.visualize_agent(ql_agent)
elif viz == "learning":
# Run experiment and make plot.
mdp.visualize_learning(ql_agent)
class StochasticSAPolicy(object):
def __init__(self, state_abstr, mdp):
self.state_abstr = state_abstr
self.mdp = mdp
self.vi = ValueIteration(mdp)
self.vi.run_vi()
def policy(self, state):
'''
Args:
(simple_rl.State)
Returns:
(str): An action
Summary:
Chooses an action among the optimal actions in the cluster. That is, roughly:
\pi(a \mid s_a) \sim Pr_{s_g \in s_a} (a = a^*(s_a))
'''
abstr_state = self.state_abstr.phi(state)
ground_states = self.state_abstr.get_ground_states_in_abs_state(
abstr_state)
action_distr = defaultdict(float)
for s in ground_states:
a = self.vi.policy(s)
action_distr[a] += 1.0 / len(ground_states)
sampled_distr = np.random.multinomial(1,
action_distr.values()).tolist()
indices = [i for i, x in enumerate(sampled_distr) if x > 0]
return action_distr.keys()[indices[0]]
def get_transition_matrix(mdp):
'''
Args:
mdp
Returns:
T (list): transition matrix
state_to_id (dict)
id_to_state (dict)
'''
vi = ValueIteration(mdp) # Use VI class to enumerate states
vi.run_vi()
vi._compute_matrix_from_trans_func()
# q = vi.get_q_function()
trans_matrix = vi.trans_dict
state_to_id = {}
id_to_state = {}
for i, u in enumerate(trans_matrix):
state_to_id[u] = i
id_to_state[i] = u
T = np.zeros((len(trans_matrix), len(trans_matrix)), dtype=np.int8)
for i, u in enumerate(trans_matrix):
for j, a in enumerate(trans_matrix[u]):
for k, v in enumerate(trans_matrix[u][a]):
if trans_matrix[u][a][v] > 0:
T[i][state_to_id[v]] = 1 # Node index starts from 1 (Minizinc is 1-indexed language)
return T, state_to_id, id_to_state
def main():
# Setup MDP, Agents.
mdp = GridWorldMDP(width=4, height=3, init_loc=(1, 1), goal_locs=[(4, 3)], lava_locs=[(4, 2)], gamma=0.95, walls=[(2, 2)], slip_prob=0.1)
ql_agent = QLearningAgent(mdp.get_actions(), epsilon=0.2, alpha=0.2)
viz = parse_args()
# Choose viz type.
viz = "value"
if viz == "value":
# --> Color corresponds to higher value.
# Run experiment and make plot.
mdp.visualize_value()
elif viz == "policy":
# Viz policy
value_iter = ValueIteration(mdp)
value_iter.run_vi()
policy = value_iter.policy
mdp.visualize_policy(policy)
elif viz == "agent":
# --> Press <spacebar> to advance the agent.
# First let the agent solve the problem and then visualize the agent's resulting policy.
print("\n", str(ql_agent), "interacting with", str(mdp))
run_single_agent_on_mdp(ql_agent, mdp, episodes=500, steps=200)
mdp.visualize_agent(ql_agent)
elif viz == "learning":
# --> Press <r> to reset.
# Show agent's interaction with the environment.
mdp.visualize_learning(ql_agent, delay=0.005, num_ep=500, num_steps=200)
elif viz == "interactive":
# Press <1>, <2>, <3>, and so on to execute action 1, action 2, etc.
mdp.visualize_interaction()
def update_policy(self):
avg_mdp_vi = ValueIteration(compute_avg_mdp(self.active_mdp_distr),
delta=0.0001,
max_iterations=1000,
sample_rate=5)
avg_mdp_vi.run_vi()
self.policy = avg_mdp_vi.policy
def main():
ap_map = {'a': (2, 2), 'b': (6, 3), 'c': (5, 3), 'd': (4, 2)}
ltlformula = 'F (b & Fa)'
# Setup MDP, Agents.
mdp = LTLGridWorldMDP(ltltask=ltlformula,
ap_map=ap_map,
width=6,
height=6,
goal_locs=[(6, 6)],
slip_prob=0.2)
mdp.automata.subproblem_flag = 0
mdp.automata.subproblem_stay = 1
mdp.automata.subproblem_goal = 0
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
print(ltlformula)
f = open('/Users/romapatel/Desktop/actions.tsv', 'w+')
for item in state_seq:
f.write(str(item) + '\n')
f.close()
model = None
ltl_visualiser(model)
def main():
# Setup MDP, Agents.
mdp = FourRoomMDP(11, 11, goal_locs=[(11, 11)], gamma=0.9, step_cost=0.0)
ql_agent = QLearningAgent(mdp.get_actions(), epsilon=0.2, alpha=0.4)
viz = parse_args()
# Choose viz type.
viz = "learning"
if viz == "value":
# Run experiment and make plot.
mdp.visualize_value()
elif viz == "policy":
# Viz policy
value_iter = ValueIteration(mdp)
value_iter.run_vi()
policy = value_iter.policy
mdp.visualize_policy(policy)
elif viz == "agent":
# Solve problem and show agent interaction.
print("\n", str(ql_agent), "interacting with", str(mdp))
run_single_agent_on_mdp(ql_agent, mdp, episodes=500, steps=200)
mdp.visualize_agent(ql_agent)
elif viz == "learning":
# Run experiment and make plot.
mdp.visualize_learning(ql_agent)
elif viz == "interactive":
mdp.visualize_interaction()
def get_l1_policy(start_room=None,
goal_room=None,
mdp=None,
starting_items=None,
goal_items=None,
actions=None,
doors=None,
rooms=None):
if mdp is None:
mdp = FourRoomL1MDP(start_room,
goal_room,
starting_items=starting_items,
goal_items=goal_items,
actions=actions,
doors=doors,
rooms=rooms)
vi = ValueIteration(mdp)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(mdp.init_state)
print 'Plan for {}:'.format(mdp)
for i in range(len(action_seq)):
print "\tpi[{}] -> {}".format(state_seq[i], action_seq[i])
policy[state_seq[i]] = action_seq[i]
return policy
def get_optimal_policies(environment):
'''
Args:
environment (simple_rl.MDPDistribution)
Returns:
(list)
'''
# Make State Abstraction
approx_qds_test = get_sa(environment,
indic_func=ind_funcs._q_eps_approx_indicator,
epsilon=0.05)
# True Optimal
true_opt_vi = ValueIteration(environment)
true_opt_vi.run_vi()
opt_agent = FixedPolicyAgent(true_opt_vi.policy, "$\pi^*$")
# Optimal Abstraction
opt_det_vi = AbstractValueIteration(environment,
state_abstr=approx_qds_test,
sample_rate=30)
opt_det_vi.run_vi()
opt_det_agent = FixedPolicyAgent(opt_det_vi.policy, name="$\pi_{\phi}^*$")
stoch_policy_obj = StochasticSAPolicy(approx_qds_test, environment)
stoch_agent = FixedPolicyAgent(stoch_policy_obj.policy,
"$\pi(a \mid s_\phi )$")
ql_agents = [opt_agent, stoch_agent, opt_det_agent]
return ql_agents
def main():
ap_map = {'a': (2, 2), 'b': (6, 3), 'c': (5, 3), 'd': (4, 2)}
print('Automic propositions, ', ap_map)
ltlformula = 'F (b & Fa)'
print('LTL Formula, ', ltlformula)
# Setup MDP, Agents.
print('translatinggg')
a = spot.translate('(a U b) & GFc & GFd', 'BA', 'complete')
a.show("v" "")
return
mdp = LTLGridWorldMDP(ltltask=ltlformula,
ap_map=ap_map,
width=6,
height=6,
goal_locs=[(6, 6)],
slip_prob=0.2)
mdp.automata.subproblem_flag = 0
mdp.automata.subproblem_stay = 1
mdp.automata.subproblem_goal = 0
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
print('Value iteration')
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
def main():
# Setup MDP, Agents.
mdp = GridWorldMDP(width=6, height=6, goal_locs=[(6, 6)], slip_prob=0.2)
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
def get_l1_policy(domain):
vi = ValueIteration(domain, sample_rate=1)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(domain.init_state)
print('Plan for {}:'.format(domain))
for i in range(len(action_seq)):
print("\tpi[{}] -> {}\n".format(state_seq[i], action_seq[i]))
policy[state_seq[i]] = action_seq[i]
return policy
def get_l1_policy(start_room=None, goal_room=None, mdp=None):
if mdp is None:
mdp = CubeL1MDP(start_room, goal_room)
vi = ValueIteration(mdp)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(mdp.init_state)
print('Plan for {}:'.format(mdp))
for i in range(len(action_seq)):
print("\tpi[{}] -> {}".format(state_seq[i], action_seq[i]))
policy[state_seq[i]] = action_seq[i]
return policy
def get_l1_policy(oomdp=None):
if oomdp is None:
oomdp = TaxiL1OOMDP()
vi = ValueIteration(oomdp, sample_rate=1)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(oomdp.init_state)
print('Plan for {}:'.format(oomdp))
for i in range(len(action_seq)):
print("\tpi[{}] -> {}\n".format(state_seq[i], action_seq[i]))
policy[state_seq[i]] = action_seq[i]
return policy
def main():
# Setup MDP, Agents.
size = 5
agent = {
"x": 1,
"y": 1,
"dx": 1,
"dy": 0,
"dest_x": size,
"dest_y": size,
"has_block": 0
}
blocks = [{"x": size, "y": 1}]
lavas = [{
"x": x,
"y": y
} for x, y in map(lambda z: (z + 1, (size + 1) / 2), xrange(size))]
mdp = TrenchOOMDP(size, size, agent, blocks, lavas)
ql_agent = QLearnerAgent(actions=mdp.get_actions())
rand_agent = RandomAgent(actions=mdp.get_actions())
# Run experiment and make plot.
# run_agents_on_mdp([ql_agent, rand_agent], mdp, instances=30, episodes=250, steps=250)
vi = ValueIteration(mdp, delta=0.0001, max_iterations=5000)
iters, val = vi.run_vi()
print " done."
states = vi.get_states()
num_states = len(states)
print num_states, states
def main():
ap_map = {'a': (2,2),'b': (6,3), 'c': (5,3), 'd': (4,2)}
ltlformula = 'F (b & Fa)'
# Setup MDP, Agents.
mdp = LTLGridWorldMDP(ltltask=ltlformula, ap_map=ap_map, width=6, height=6, goal_locs=[(6, 6)], slip_prob=0.2)
mdp.automata.subproblem_flag = 0
mdp.automata.subproblem_stay = 1
mdp.automata.subproblem_goal = 0
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
def generate_agent(mdp_class, data_loc, mdp_parameters, visualize=False):
try:
with open('models/' + data_loc + '/vi_agent.pickle', 'rb') as f:
mdp_agent, vi_agent = pickle.load(f)
except:
mdp_agent = make_mdp.make_custom_mdp(mdp_class, mdp_parameters)
vi_agent = ValueIteration(mdp_agent, sample_rate=1)
vi_agent.run_vi()
with open('models/' + data_loc + '/vi_agent.pickle', 'wb') as f:
pickle.dump((mdp_agent, vi_agent), f)
# Visualize agent
if visualize:
fixed_agent = FixedPolicyAgent(vi_agent.policy)
mdp_agent.visualize_agent(fixed_agent)
mdp_agent.reset() # reset the current state to the initial state
mdp_agent.visualize_interaction()
def get_l1_policy(start_room=None, goal_room=None, mdp=None):
if mdp is None:
mdp = FourRoomL1MDP(start_room,
goal_room,
starting_items=[2, 0],
goal_items=[2,
1]) #room 2, light off =0, light on =1
vi = ValueIteration(mdp)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(mdp.init_state)
print 'Plan for {}:'.format(mdp)
for i in range(len(action_seq)):
print "\tpi[{}] -> {}".format(state_seq[i], action_seq[i])
policy[state_seq[i]] = action_seq[i]
return policy
def main():
args = parse_args()
mdp = generate_MDP(args.width, args.height, args.i_loc, args.g_loc,
args.l_loc, args.gamma, args.Walls, args.slip)
ql_agent = QLearningAgent(mdp.get_actions(),
epsilon=args.epsilon,
alpha=args.alpha,
explore=args.explore,
anneal=args.anneal)
viz = args.mode
if viz == "value":
# --> Color corresponds to higher value.
# Run experiment and make plot.
mdp.visualize_value()
elif viz == "policy":
# Viz policy
value_iter = ValueIteration(mdp)
value_iter.run_vi()
mdp.visualize_policy_values(
(lambda state: value_iter.policy(state)),
(lambda state: value_iter.value_func[state]))
elif viz == "agent":
# --> Press <spacebar> to advance the agent.
# First let the agent solve the problem and then visualize the agent's resulting policy.
print("\n", str(ql_agent), "interacting with", str(mdp))
rand_agent = RandomAgent(actions=mdp.get_actions())
run_agents_on_mdp([rand_agent, ql_agent],
mdp,
open_plot=True,
episodes=60,
steps=200,
instances=5,
success_reward=1)
# mdp.visualize_agent(ql_agent)
elif viz == "learning":
# --> Press <r> to reset.
# Show agent's interaction with the environment.
mdp.visualize_learning(ql_agent,
delay=0.005,
num_ep=500,
num_steps=200)
def main():
mdp1 = GridWorldMDP(width=2,
height=1,
init_loc=(1, 1),
goal_locs=[(2, 1)],
slip_prob=0.5,
gamma=0.5)
vi = ValueIteration(mdp1)
iters, value = vi.run_vi()
print("value=", value)
def run_plain_pMDP(init_loc, ltl_formula, cube_env, ap_maps, verbose=False):
start_time = time.time()
mdp = RoomCubePlainMDP(init_loc=init_loc,
ltl_formula=ltl_formula,
env_file=[cube_env],
ap_maps=ap_maps)
value_iter = ValueIteration(mdp, sample_rate=1, max_iterations=50)
value_iter.run_vi()
# Value Iteration
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
computing_time = time.time() - start_time
# Print
if verbose:
print("=====================================================")
print("Plain: Plan for ", ltl_formula)
for i in range(len(action_seq)):
room_number, floor_number = mdp._get_abstract_number(state_seq[i])
print("\t {} in room {} on the floor {}, {}".format(
state_seq[i], room_number, floor_number, action_seq[i]))
room_number, floor_number = mdp._get_abstract_number(state_seq[-1])
print("\t {} in room {} on the floor {}".format(
state_seq[-1], room_number, floor_number))
# success?
if len(state_seq) <= 1:
flag_success = -1
else:
if mdp.automata.aut_spot.state_is_accepting(state_seq[-1].q):
flag_success = 1
else:
flag_success = 0
return computing_time, len(
action_seq
), flag_success, state_seq, action_seq, value_iter.get_num_backups_in_recent_run(
)
def main():
# Setup MDP, Agents.
mdp = FourRoomMDP(9, 9, goal_locs=[(9, 9)], gamma=0.95)
ql_agent = QLearnerAgent(mdp.get_actions())
viz = parse_args()
if viz == "value":
# Run experiment and make plot.
mdp.visualize_value()
elif viz == "policy":
# Viz policy
vi = ValueIteration(mdp)
vi.run_vi()
policy = vi.policy
mdp.visualize_policy(policy)
elif viz == "agent":
# Solve problem and show agent interaction.
print "\n", str(ql_agent), "interacting with", str(mdp)
run_single_agent_on_mdp(ql_agent, mdp, episodes=500, steps=200)
mdp.visualize_agent(ql_agent)
def make_multitask_sa_info_sa(mdp_distr, beta, is_deterministic_ib=False):
'''
Args:
mdp_distr (simple_rl.MDPDistribution)
beta (float)
is_deterministic_ib (float)
Returns:
(simple_rl.StateAbstraction)
'''
master_sa = None
all_state_absr = []
for mdp in mdp_distr.get_all_mdps():
# 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))
# Get abstraction.
pmf_s_phi, phi_pmf, abstr_policy_pmf = run_info_sa(
mdp,
demo_policy,
beta=beta,
is_deterministic_ib=is_deterministic_ib)
crisp_sa = convert_prob_sa_to_sa(ProbStateAbstraction(phi_pmf))
all_state_absr.append(crisp_sa)
# Make master state abstr by intersection.
vi = ValueIteration(mdp_distr.get_all_mdps()[0])
ground_states = vi.get_states()
master_sa = sa_helpers.merge_state_abstr(all_state_absr, ground_states)
return master_sa
def main(open_plot=True):
# Setup MDP.
args = parse_args()
mdp = generate_MDP(args.width, args.height, args.i_loc, args.g_loc,
args.l_loc, args.gamma, args.Walls, args.slip)
if args.visualize:
value_iter = ValueIteration(mdp)
value_iter.run_vi()
mdp.visualize_policy_values(
(lambda state: value_iter.policy(state)),
(lambda state: value_iter.value_func[state]))
else:
custom_q = parse_custom_q_table(args.custom_q, args.default_q)
agents = []
for agent in args.agents:
if agent == 'q_learning':
agents.append(QLearningAgent(actions=mdp.get_actions()))
elif agent == 'potential_q':
agents.append(
QLearningAgent(actions=mdp.get_actions(),
custom_q_init=custom_q,
name="Potential_Q"))
elif agent == 'random':
agents.append(RandomAgent(actions=mdp.get_actions()))
elif agent == 'rmax':
agents.append(RMaxAgent(mdp.get_actions()))
# Run experiment and make plot.
run_agents_on_mdp(agents,
mdp,
instances=1,
episodes=100,
steps=100,
open_plot=open_plot,
verbose=True)
class MonotoneLowerBound(Planner):
def __init__(self, mdp, name='MonotoneUpperBound'):
relaxed_mdp = MonotoneLowerBound._construct_deterministic_relaxation_mdp(mdp)
Planner.__init__(self, relaxed_mdp, name)
self.vi = ValueIteration(relaxed_mdp)
self.states = self.vi.get_states()
self.vi._compute_matrix_from_trans_func()
self.vi.run_vi()
self.lower_values = self._construct_lower_values()
@staticmethod
def _construct_deterministic_relaxation_mdp(mdp):
relaxed_mdp = copy.deepcopy(mdp)
relaxed_mdp.set_slip_prob(0.0)
return relaxed_mdp
def _construct_lower_values(self):
values = defaultdict()
for state in self.states:
values[state] = self.vi.get_value(state)
return values
def main():
# Grab experiment params.
# Switch between Upworld and Trench
mdp_class = "upworld"
# mdp_class = "trench"
grid_lim = 20 if mdp_class == 'upworld' else 7
gamma = 0.95
vanilla_file = "vi.csv"
sa_file = "vi-$\phi_{Q_d^*}.csv"
file_prefix = "results/planning-" + mdp_class + "/"
clear_files(dir_name=file_prefix)
for grid_dim in xrange(3, grid_lim):
# ======================
# == Make Environment ==
# ======================
environment = make_mdp.make_mdp(mdp_class=mdp_class, grid_dim=grid_dim)
environment.set_gamma(gamma)
# =======================
# == Make Abstractions ==
# =======================
sa_qds = get_sa(environment,
indic_func=ind_funcs._q_disc_approx_indicator,
epsilon=0.01)
# ============
# == Run VI ==
# ============
vanilla_vi = ValueIteration(environment, delta=0.0001, sample_rate=15)
sa_vi = AbstractValueIteration(ground_mdp=environment,
state_abstr=sa_qds)
print "Running VIs."
start_time = time.clock()
vanilla_iters, vanilla_val = vanilla_vi.run_vi()
vanilla_time = round(time.clock() - start_time, 2)
start_time = time.clock()
sa_iters, sa_val = sa_vi.run_vi()
sa_time = round(time.clock() - start_time, 2)
print "vanilla", vanilla_iters, vanilla_val, vanilla_time
print "sa:", sa_iters, sa_val, sa_time
write_datum(file_prefix + "iters/" + vanilla_file, vanilla_iters)
write_datum(file_prefix + "iters/" + sa_file, sa_iters)
write_datum(file_prefix + "times/" + vanilla_file, vanilla_time)
write_datum(file_prefix + "times/" + sa_file, sa_time)
class MonotoneLowerBound(Planner):
def __init__(self, mdp, name='MonotoneUpperBound'):
relaxed_mdp = MonotoneLowerBound._construct_deterministic_relaxation_mdp(
mdp)
Planner.__init__(self, relaxed_mdp, name)
self.vi = ValueIteration(relaxed_mdp)
self.states = self.vi.get_states()
self.vi._compute_matrix_from_trans_func()
self.vi.run_vi()
self.lower_values = self._construct_lower_values()
@staticmethod
def _construct_deterministic_relaxation_mdp(mdp):
relaxed_mdp = copy.deepcopy(mdp)
relaxed_mdp.set_slip_prob(0.0)
return relaxed_mdp
def _construct_lower_values(self):
values = defaultdict()
for state in self.states:
values[state] = self.vi.get_value(state)
return values
def evaluate_multitask_sa(multitask_sa, mdp_distr, samples=10):
'''
Args:
multitask_sa (simple_rl.abstraction.StateAbstraction)
mdp_distr (simple_rl.mdp.MDPDistribution)
samples (float)
'''
# Average value over @samples.
avg_opt_val = 0.0
avg_abstr_opt_val = 0.0
for i in range(samples):
mdp = mdp_distr.sample()
# Optimal Policy.
vi = ValueIteration(mdp)
vi.run_vi()
opt_agent = FixedPolicyAgent(vi.policy)
# Evaluate Optimal Abstract Policy.
# abstr_mdp = make_abstr_mdp(mdp, state_abstr=multitask_sa)
# abstr_vi = ValueIteration(abstr_mdp, sample_rate=20)
abstr_vi = AbstractValueIteration(mdp, state_abstr=multitask_sa)
abstr_vi.run_vi()
abstr_opt_policy_mapper = SAVI(multitask_sa, abstr_vi.policy)
abstr_opt_agent = FixedPolicyAgent(abstr_opt_policy_mapper.policy,
"abstract")
# Compare.
avg_opt_val += evaluate_agent(opt_agent, mdp) / samples
avg_abstr_opt_val += evaluate_agent(abstr_opt_agent, mdp) / samples
print "Ground:", multitask_sa.get_num_ground_states(), round(
avg_opt_val, 4)
print "Abstract:", multitask_sa.get_num_abstr_states(), round(
avg_abstr_opt_val, 4)
print
def _make_mini_mdp_option_policy(mini_mdp):
'''
Args:
mini_mdp (MDP)
Returns:
Policy
'''
# Solve the MDP defined by the terminal abstract state.
mini_mdp_vi = ValueIteration(mini_mdp, delta=0.005, max_iterations=500, sample_rate=20)
iters, val = mini_mdp_vi.run_vi()
o_policy_dict = make_dict_from_lambda(mini_mdp_vi.policy, mini_mdp_vi.get_states())
o_policy = PolicyFromDict(o_policy_dict)
return o_policy.get_action, mini_mdp_vi
def main():
# Make MDP.
grid_dim = 11
mdp = FourRoomMDP(width=grid_dim, height=grid_dim, init_loc=(1, 1), slip_prob=0.05, goal_locs=[(grid_dim, grid_dim)], gamma=0.99)
# Experiment Type.
exp_type = "learn_w_abstr"
# For comparing policies and visualizing.
beta = 1
is_deterministic_ib = True
is_agent_in_control = True
# For main plotting experiment.
beta_range = list(chart_utils.drange(0.0, 4.0, 1.0))
instances = 1
# Get demo policy.
vi = ValueIteration(mdp)
_, val = vi.run_vi()
# Epsilon greedy policy
demo_policy = get_lambda_policy(make_det_policy_eps_greedy(vi.policy, vi.get_states(), mdp.get_actions(), epsilon=0.1))
if exp_type == "plot_info_sa_val_and_num_states":
# Makes the main two plots.
make_info_sa_val_and_size_plots(mdp, demo_policy, beta_range, instances=instances, is_agent_in_control=is_agent_in_control)
elif exp_type == "compare_policies":
# Makes a plot comparing value of pi-phi combo from info_sa with \pi_d.
info_sa_compare_policies(mdp, demo_policy, beta=beta, is_deterministic_ib=is_deterministic_ib, is_agent_in_control=is_agent_in_control)
elif exp_type == "visualize_info_sa_abstr":
# Visualize the state abstraction found by info_sa.
info_sa_visualize_abstr(mdp, demo_policy, beta=beta, is_deterministic_ib=is_deterministic_ib, is_agent_in_control=is_agent_in_control)
elif exp_type == "learn_w_abstr":
# Run learning experiments for different settings of \beta.
learn_w_abstr(mdp, demo_policy, is_deterministic_ib=is_deterministic_ib)
elif exp_type == "planning":
info_sa_planning_experiment()
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 draw_state(screen,
cleanup_mdp,
state,
policy=None,
action_char_dict={},
show_value=False,
agent=None,
draw_statics=False,
agent_shape=None):
'''
Args:
screen (pygame.Surface)
grid_mdp (MDP)
state (State)
show_value (bool)
agent (Agent): Used to show value, by default uses VI.
draw_statics (bool)
agent_shape (pygame.rect)
Returns:
(pygame.Shape)
'''
# Make value dict.
val_text_dict = defaultdict(lambda: defaultdict(float))
if show_value:
if agent is not None:
# Use agent value estimates.
for s in agent.q_func.keys():
val_text_dict[s.x][s.y] = agent.get_value(s)
else:
# Use Value Iteration to compute value.
vi = ValueIteration(cleanup_mdp)
vi.run_vi()
for s in vi.get_states():
val_text_dict[s.x][s.y] = vi.get_value(s)
# Make policy dict.
policy_dict = defaultdict(lambda: defaultdict(str))
if policy:
vi = ValueIteration(cleanup_mdp)
vi.run_vi()
for s in vi.get_states():
policy_dict[s.x][s.y] = policy(s)
# Prep some dimensions to make drawing easier.
scr_width, scr_height = screen.get_width(), screen.get_height()
width_buffer = scr_width / 10.0
height_buffer = 30 + (scr_height / 10.0) # Add 30 for title.
width = cleanup_mdp.width
height = cleanup_mdp.height
cell_width = (scr_width - width_buffer * 2) / width
cell_height = (scr_height - height_buffer * 2) / height
# goal_locs = grid_mdp.get_goal_locs()
# lava_locs = grid_mdp.get_lavacc_locs()
font_size = int(min(cell_width, cell_height) / 4.0)
reg_font = pygame.font.SysFont("CMU Serif", font_size)
cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2)
# room_locs = [(x + 1, y + 1) for room in cleanup_mdp.rooms for (x, y) in room.points_in_room]
door_locs = set([(door.x + 1, door.y + 1) for door in state.doors])
# Draw the static entities.
# print(draw_statics)
# draw_statics = True
# if draw_statics:
# For each row:
for i in range(width):
# For each column:
for j in range(height):
top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j
r = pygame.draw.rect(screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3)
# if policy and not grid_mdp.is_wall(i+1, height - j):
if policy and (i + 1, height - j) in cleanup_mdp.legal_states:
a = policy_dict[i + 1][height - j]
if a not in action_char_dict:
text_a = a
else:
text_a = action_char_dict[a]
text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(
top_left_point[1] + cell_height / 3.0)
text_rendered_a = cc_font.render(text_a, True, (46, 49, 49))
screen.blit(text_rendered_a, text_center_point)
# if show_value and not grid_mdp.is_wall(i+1, grid_mdp.height - j):
if show_value and (i + 1, height - j) in cleanup_mdp.legal_states:
# Draw the value.
val = val_text_dict[i + 1][height - j]
color = mdpv.val_to_color(val)
pygame.draw.rect(screen, color, top_left_point + (cell_width, cell_height), 0)
# text_center_point = int(top_left_point[0] + cell_width/2.0 - 10), int(top_left_point[1] + cell_height/7.0)
# text = str(round(val,2))
# text_rendered = reg_font.render(text, True, (46, 49, 49))
# screen.blit(text_rendered, text_center_point)
# if grid_mdp.is_wall(i+1, grid_mdp.height - j):
if (i + 1, height - j) not in cleanup_mdp.legal_states:
# Draw the walls.
top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5
pygame.draw.rect(screen, (94, 99, 99), top_left_point + (cell_width - 10, cell_height - 10), 0)
if (i + 1, height - j) in door_locs:
# Draw door
# door_color = (66, 83, 244)
door_color = (0, 0, 0)
top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5
pygame.draw.rect(screen, door_color, top_left_point + (cell_width - 10, cell_height - 10), 0)
else:
room = cleanup_mdp.check_in_room(state.rooms, i + 1 - 1, height - j - 1) # Minus 1 for inconsistent x, y
if room:
top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5
room_rgb = _get_rgb(room.color)
pygame.draw.rect(screen, room_rgb, top_left_point + (cell_width - 10, cell_height - 10), 0)
block = cleanup_mdp.find_block(state.blocks, i + 1 - 1, height - j - 1)
# print(state)
# print(block)
if block:
circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0)
block_rgb = _get_rgb(block.color)
pygame.draw.circle(screen, block_rgb, circle_center, int(min(cell_width, cell_height) / 4.0))
# Current state.
if not show_value and (i + 1, height - j) == (state.x + 1, state.y + 1) and agent_shape is None:
tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0)
agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 8)
if agent_shape is not None:
# Clear the old shape.
pygame.draw.rect(screen, (255, 255, 255), agent_shape)
top_left_point = width_buffer + cell_width * ((state.x + 1) - 1), height_buffer + cell_height * (
height - (state.y + 1))
tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0)
# Draw new.
# if not show_value or policy is not None:
agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 16)
pygame.display.flip()
return agent_shape
