def runRewardIRL(self,N=5):
opt = deepcopy(self.opt_template)
dist = self.getIRLTDist(self.env_template["consumable"],N=N)
bdist = self.getIRLDist(N=N, rand=True)
dist = [d-bdist for d in dist]
print dist
domain = self.createStateDomain(waypoints=self.env_template["consumable"],
rewardFunction=lambda x,y,z,w: ConsumableGridWorldIRL.rewardIRL(x,y,z,w,dist,self.env_template["consumable"]))
opt["domain"] = domain
representation = IncrementalTabular(domain, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
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.)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain = self.createStateDomain(self.env_template["consumable"]),
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 runIRL(self, N=5):
opt = deepcopy(self.opt_template)
dist = self.getIRLDist(N=N)
bdist = self.getIRLDist(N=N, rand=True)
#print dist-bdist
domain = self.createMarkovDomain(
rewardFunction=lambda x, y, z, w: ConsumableGridWorldIRL.
maxEntReward(x, y, z, w, dist - bdist))
opt["domain"] = domain
representation = IncrementalTabular(
domain, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
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.)
performance_domain = self.createMarkovDomain()
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain=performance_domain,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]), np.sum(
experiment.result["return"])
def runTIRL(self, N=5, w=2, pruning=0.5):
opt = deepcopy(self.opt_template)
dist = self.getIRLDist(N=N)
ac = self.getTSCWaypoints(N, w, pruning)
domain = self.createStateDomain(
waypoints=ac,
rewardFunction=lambda x, y, z, w: ConsumableGridWorldIRL.rewardIRL(
x, y, z, w, dist))
opt["domain"] = domain
representation = IncrementalTabular(
domain, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
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.)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain=self.createStateDomain(
waypoints=self.env_template["consumable"]),
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]), np.sum(
experiment.result["return"])
def get_demonstrations(demonstration_per_policy, max_policy_iter,
num_policy_demo_checks, agent):
"""return demonstrations generated from the parallel parking car rlpy simulator"""
opt = {}
opt["exp_id"] = 1
# opt["path"] = "./Results/gridworld2"
opt["checks_per_policy"] = 5
opt["max_steps"] = 1000000
opt["num_policy_checks"] = 1000
exp = 0.3
discretization = 20
walls = [(-1, -0.3, 0.1, 0.3)]
domain = RCIRL([(-0.1, -0.25)],
wallArray=walls,
noise=0,
rewardFunction=RCIRL.rcreward)
domain.episodeCap = 200
# Representation 10
representation = RBF(domain,
num_rbfs=1000,
resolution_max=25,
resolution_min=25,
const_feature=False,
normalize=True,
seed=1) #discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
# Agent
# opt["agent"]=agent
opt["agent"] = Q_Learning(representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.7,
learn_rate_decay_mode="boyan",
boyan_N0=700,
lambda_=0.)
opt["domain"] = domain
pdomain = RCIRL([(-0.1, -0.25)], wallArray=walls, noise=0)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain=pdomain,
visualize_learning=False,
visualize_performance=1)
# return experiment
return map(lambda x: map(lambda y: np.array(y), x),
experiment.all_experiment_list)
def grid_world1_reward(exp_id=2, 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
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)
opt["domain"] = domain
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=exp)
# 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.)
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 runSliding(self,k=3):
opt = deepcopy(self.opt_template)
domain = self.createSlidingDomain(k)
opt["domain"] = domain
representation = IncrementalTabular(domain, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
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.)
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 get_demonstrations(demonstration_per_policy,max_policy_iter,num_policy_demo_checks,agent):
"""return demonstrations generated from the parallel parking car rlpy simulator"""
opt = {}
opt["exp_id"] = 1
# opt["path"] = "./Results/gridworld2"
opt["checks_per_policy"] = 5
opt["max_steps"] = 1000000
opt["num_policy_checks"] = 1000
exp = 0.3
discretization = 20
walls = [(-1, -0.3, 0.1, 0.3)]
domain = RCIRL([(-0.1, -0.25)],
wallArray=walls,
noise=0, rewardFunction=RCIRL.rcreward)
domain.episodeCap = 200
# Representation 10
representation = RBF(domain, num_rbfs=1000,resolution_max=25, resolution_min=25,
const_feature=False, normalize=True, seed=1) #discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=0.3)
# Agent
# opt["agent"]=agent
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.7,
learn_rate_decay_mode="boyan", boyan_N0=700,
lambda_=0.)
opt["domain"] = domain
pdomain = RCIRL([(-0.1, -0.25)],
wallArray=walls,
noise=0)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain = pdomain,
visualize_learning=False,
visualize_performance=1)
# return experiment
return map(lambda x:map(lambda y:np.array(y),x),experiment.all_experiment_list)
def grid_world1_reward(exp_id=2, 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
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)
opt["domain"] = domain
# Representation
representation = Tabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=exp)
# 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.)
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 runSliding(self, k=3):
opt = deepcopy(self.opt_template)
domain = self.createSlidingDomain(k)
opt["domain"] = domain
representation = IncrementalTabular(
domain, discretization=self.env_template["discretization"])
policy = eGreedy(representation, epsilon=self.env_template["exp"])
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.)
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"])
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"])
walls = [(-1, -0.3, 0.1, 0.3)]
domain = RCIRL([(-0.1, -0.25)],
wallArray=walls,
noise=0, rewardFunction=RCIRL.rcreward)
domain.episodeCap = 200
# Representation 10
representation = RBF(domain, num_rbfs=1000,resolution_max=25, resolution_min=25,
const_feature=False, normalize=True, seed=1) #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=0.7,
learn_rate_decay_mode="boyan", boyan_N0=700,
lambda_=0.)
opt["domain"] = domain
pdomain = RCIRL([(-0.1, -0.25)],
wallArray=walls,
noise=0)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain = pdomain,
visualize_learning=False,
visualize_performance=1)
experiment.save()
domain.episodeCap = 200
# Representation 10
representation = RBF(domain,
num_rbfs=1000,
resolution_max=25,
resolution_min=25,
const_feature=False,
normalize=True,
seed=1) #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=0.7,
learn_rate_decay_mode="boyan",
boyan_N0=700,
lambda_=0.)
opt["domain"] = domain
pdomain = RCIRL([(-0.1, -0.25)], wallArray=walls, noise=0)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
performance_domain=pdomain,
visualize_learning=False,
visualize_performance=1)
experiment.save()
