def grid_world1_trp(exp_id=4, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 20
# Domain:
maze = os.path.join(ConsumableGridWorld.default_map_dir, '10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
binary=True,
noise=noise)
#domain = Pinball(noise=0.3)
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
d = GoalPathPlanner(domain, representation,policy)
trajs = d.generateTrajectories(N=5)
a = TransitionStateClustering(window_size=2)
for t in trajs:
N = len(t)
demo = np.zeros((N,2))
for i in range(0,N):
demo[i,:] = t[i][0:2]
a.addDemonstration(demo)
a.fit(normalize=False, pruning=0.5)
ac = [(round(a.means_[0][0]),round(a.means_[0][1])) for a in a.model]
print ac
#reinitialize
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.statePassageEncoding(x,ac,5), noise=noise)
representation = IncrementalTabular(domain, discretization=discretization)
policy = eGreedy(representation, epsilon=0.3)
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan", boyan_N0=100,
lambda_=0.)
opt["domain"] = domain
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]),np.sum(experiment.result["return"])
def getIRLDist(self,N=5, rand=False):
sd = self.createStateDomain(self.env_template["consumable"])
representation = IncrementalTabular(sd, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
if not rand:
d = GoalPathPlanner(sd,representation,policy, steps=self.opt_template["max_steps"])
else:
d = GoalPathPlanner(sd,representation,policy, steps=100)
trajs = d.generateTrajectories(N=N)
return calculateStateDist(np.shape(sd.map), trajs)
def getIRLTDist(self, waypoints, N=5, rand=False):
sd = self.createStateDomain(self.env_template["consumable"])
representation = IncrementalTabular(
sd, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
if not rand:
d = GoalPathPlanner(sd,
representation,
policy,
steps=self.opt_template["max_steps"])
else:
d = GoalPathPlanner(sd, representation, policy, steps=100)
trajs = d.generateTrajectories(N=N)
return calculateStateTemporalDist(np.shape(sd.map), trajs, waypoints)
def getTSCWaypoints(self,N=5, w=2, pruning=0.5):
sd = self.createStateDomain(self.env_template["consumable"])
representation = IncrementalTabular(sd, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
d = GoalPathPlanner(sd,representation,policy, steps=self.opt_template["max_steps"])
trajs = d.generateTrajectories(N=N)
a = TransitionStateClustering(window_size=w)
for t in trajs:
N = len(t)
demo = np.zeros((N,2))
for i in range(0,N):
demo[i,:] = t[i][0:2]
a.addDemonstration(demo)
a.fit(normalize=False, pruning=pruning)
dist = calculateStateDist(np.shape(sd.map), trajs)
return discrete2DClustersToPoints(a.model, dist, radius=1)
def getTSCWaypoints(self, N=5, w=2, pruning=0.5):
sd = self.createStateDomain(self.env_template["consumable"])
representation = IncrementalTabular(
sd, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
d = GoalPathPlanner(sd,
representation,
policy,
steps=self.opt_template["max_steps"])
trajs = d.generateTrajectories(N=N)
a = TransitionStateClustering(window_size=w)
for t in trajs:
N = len(t)
demo = np.zeros((N, 2))
for i in range(0, N):
demo[i, :] = t[i][0:2]
a.addDemonstration(demo)
a.fit(normalize=False, pruning=pruning)
dist = calculateStateDist(np.shape(sd.map), trajs)
return discrete2DClustersToPoints(a.model, dist, radius=1)
def gridworld1_tirl(exp_id=7, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 400
# Domain:
maze = os.path.join(ConsumableGridWorld.default_map_dir,
'10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
noise=noise,
binary=True)
#domain = Pinball(noise=0.3)
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
# Agent
opt["agent"] = Q_Learning(representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan",
boyan_N0=100,
lambda_=0.)
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
d = GoalPathPlanner(domain, representation, policy)
trajs = d.generateTrajectories(N=5)
dist = calculateStateDist((10, 7), trajs)
a = TransitionStateClustering(window_size=2)
for t in trajs:
N = len(t)
demo = np.zeros((N, 2))
for i in range(0, N):
demo[i, :] = t[i][0:2]
a.addDemonstration(demo)
a.fit(normalize=False, pruning=0.5)
dist = calculateStateDist((10, 7), trajs)
ac = discrete2DClustersToPoints(a.model, dist, radius=1)
# Policy reset
policy = eGreedy(representation, epsilon=0.3)
representation = Tabular(domain, discretization=discretization)
opt["agent"] = Q_Learning(representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan",
boyan_N0=100,
lambda_=0.)
domain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
rewardFunction=lambda x, y, z, w: ConsumableGridWorldIRL.tRewardIRL(
x, y, z, w, dist, [(7, 5)]),
noise=noise)
pdomain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
noise=noise)
opt["domain"] = domain
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain=pdomain,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]), np.sum(
experiment.result["return"])
def gridworld1_rirl(exp_id=6, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 400
# Domain:
maze = os.path.join(ConsumableGridWorld.default_map_dir,
'10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
noise=noise,
binary=True)
#domain = Pinball(noise=0.3)
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
# Agent
opt["agent"] = Q_Learning(representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan",
boyan_N0=100,
lambda_=0.)
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
d = GoalPathPlanner(domain, representation, policy)
trajs = d.generateTrajectories(N=5)
dist = calculateStateDist((10, 7), trajs)
# Policy reset
policy = eGreedy(representation, epsilon=0.3)
representation = Tabular(domain, discretization=discretization)
opt["agent"] = Q_Learning(representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan",
boyan_N0=100,
lambda_=0.)
domain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
rewardFunction=lambda x, y, z, w: ConsumableGridWorldIRL.rewardIRL(
x, y, z, w, dist),
noise=noise)
pdomain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
noise=noise)
opt["domain"] = domain
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain=pdomain,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]), np.sum(
experiment.result["return"])
def grid_world1_trp(exp_id=4, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 20
# Domain:
maze = os.path.join(ConsumableGridWorld.default_map_dir,
'10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.stateVisitEncoding(
x, [(7, 5)]),
binary=True,
noise=noise)
#domain = Pinball(noise=0.3)
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
d = GoalPathPlanner(domain, representation, policy)
trajs = d.generateTrajectories(N=5)
a = TransitionStateClustering(window_size=2)
for t in trajs:
N = len(t)
demo = np.zeros((N, 2))
for i in range(0, N):
demo[i, :] = t[i][0:2]
a.addDemonstration(demo)
a.fit(normalize=False, pruning=0.5)
ac = [(round(a.means_[0][0]), round(a.means_[0][1])) for a in a.model]
print ac
#reinitialize
domain = ConsumableGridWorldIRL(
[(7, 5), (1, 2)],
mapname=maze,
encodingFunction=lambda x: ConsumableGridWorldIRL.statePassageEncoding(
x, ac, 5),
noise=noise)
representation = IncrementalTabular(domain, discretization=discretization)
policy = eGreedy(representation, epsilon=0.3)
opt["agent"] = Q_Learning(representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan",
boyan_N0=100,
lambda_=0.)
opt["domain"] = domain
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]), np.sum(
experiment.result["return"])
def gridworld1_tirl(exp_id=7, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 400
# Domain:
maze = os.path.join(ConsumableGridWorld.default_map_dir, '10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
noise=noise,
binary=True)
#domain = Pinball(noise=0.3)
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
# Agent
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan", boyan_N0=100,
lambda_=0.)
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
d = GoalPathPlanner(domain, representation,policy)
trajs = d.generateTrajectories(N=5)
dist = calculateStateDist((10,7), trajs)
a = TransitionStateClustering(window_size=2)
for t in trajs:
N = len(t)
demo = np.zeros((N,2))
for i in range(0,N):
demo[i,:] = t[i][0:2]
a.addDemonstration(demo)
a.fit(normalize=False, pruning=0.5)
dist = calculateStateDist((10,7), trajs)
ac = discrete2DClustersToPoints(a.model, dist, radius=1)
# Policy reset
policy = eGreedy(representation, epsilon=0.3)
representation = Tabular(domain, discretization=discretization)
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan", boyan_N0=100,
lambda_=0.)
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
rewardFunction= lambda x,y,z,w: ConsumableGridWorldIRL.tRewardIRL(x,y,z,w,dist,[(7,5)]),
noise=noise)
pdomain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
noise=noise)
opt["domain"] = domain
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain = pdomain,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]),np.sum(experiment.result["return"])
def gridworld1_rirl(exp_id=6, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 400
# Domain:
maze = os.path.join(ConsumableGridWorld.default_map_dir, '10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
noise=noise,
binary=True)
#domain = Pinball(noise=0.3)
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
# Agent
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan", boyan_N0=100,
lambda_=0.)
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
d = GoalPathPlanner(domain, representation,policy)
trajs = d.generateTrajectories(N=5)
dist = calculateStateDist((10,7), trajs)
# Policy reset
policy = eGreedy(representation, epsilon=0.3)
representation = Tabular(domain, discretization=discretization)
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan", boyan_N0=100,
lambda_=0.)
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
rewardFunction= lambda x,y,z,w: ConsumableGridWorldIRL.rewardIRL(x,y,z,w,dist),
noise=noise)
pdomain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.stateVisitEncoding(x,[(7,5)]),
noise=noise)
opt["domain"] = domain
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain = pdomain,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]),np.sum(experiment.result["return"])
