def update_upper_bound(self):
"""
Update the total upper bound on the Q-value function.
Called at initialization and when a new state-action pair is known.
:return: None
"""
self.update_lipschitz_upper_bounds()
self.initialize_upper_bound()
RMax.update_upper_bound(self)
def reset(self):
"""
Reset the attributes to initial state (called between instances).
:return: None
"""
self.update_memory()
RMax.reset(self)
if len(self.U_memory) > self.n_required_tasks:
self.update_max_q_init_upper_bound()
def __init__(self,
actions,
gamma=0.9,
r_max=1.,
v_max=None,
deduce_v_max=True,
n_known=None,
deduce_n_known=True,
epsilon_q=0.1,
epsilon_m=None,
delta=None,
n_states=None,
min_sampling_probability=0.1,
name="MaxQInit"):
"""
:param actions: action space of the environment
:param gamma: (float) discount factor
:param r_max: (float) known upper-bound on the reward function
:param v_max: (float) known upper-bound on the value function
:param deduce_v_max: (bool) set to True to deduce v_max from r_max
:param n_known: (int) count after which a state-action pair is considered known
(only set n_known if delta and epsilon are not defined)
:param deduce_n_known: (bool) set to True to deduce n_known from (delta, n_states, epsilon_m)
:param epsilon_q: (float) precision of value iteration algorithm for Q-value computation
:param epsilon_m: (float) precision of the learned models in L1 norm
:param delta: (float) models are learned epsilon_m-closely with probability at least 1 - delta
:param n_states: (int) number of states
:param min_sampling_probability: (float) minimum sampling probability of an environment
:param name: (str)
"""
RMax.__init__(self,
actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=deduce_v_max,
n_known=n_known,
deduce_n_known=deduce_n_known,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
name=name)
self.min_sampling_probability = min_sampling_probability
self.SA_memory = defaultdict(lambda: defaultdict(lambda: False))
self.U_memory = [] # Upper-bounds on the Q-values of previous MDPs
self.n_required_tasks = number_of_tasks_for_high_confidence_upper_bound(
delta, min_sampling_probability)
def reset(self):
"""
Reset the attributes to initial state (called between instances).
Save the previous model.
:return: None
"""
RMax.reset(self)
self.write(init=False)
# Reset recorded variables between MDPs
self.discounted_return = 0.
self.total_return = 0.
self.n_time_steps = 0
self.update_time_steps = []
def reset(self):
"""
Reset the attributes to initial state (called between instances).
Save the previous model.
:return: None
"""
# Save previously learned model
if len(self.counter) > 0 and (self.max_memory_size is None or
len(self.U_lip) < self.max_memory_size):
self.update_memory()
RMax.reset(self)
if self.estimate_distances_online:
self.update_max_distances()
self.update_upper_bound()
def experiment(p):
# Parameters
gamma = .9
n_env = 5
size = p['size']
env_distribution = make_env_distribution(
env_class='tight', n_env=n_env, gamma=gamma,
env_name=p['name'],
w=size,
h=size,
stochastic=p['stochastic']
)
actions = env_distribution.get_actions()
n_known = p['n_known']
p_min = 1. / float(n_env)
epsilon_q = .01
epsilon_m = .01
delta = .1
r_max = 1.
v_max = p['v_max']
n_states = 4
max_mem = 1
# Agents
rmax = RMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, name='RMax')
lrmax = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=None, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax')
lrmax_p01 = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=0.1, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax(Dmax=0.1)')
lrmax_p02 = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=0.2, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax(Dmax=0.2)')
maxqinit = MaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
min_sampling_probability=p_min, name='MaxQInit')
lrmaxqinit = LRMaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta,
n_states=n_states, max_memory_size=max_mem, prior=None, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMaxQInit')
lrmaxqinit_p01 = LRMaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta,
n_states=n_states, max_memory_size=max_mem, prior=0.1, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMaxQInit(Dmax=0.1)')
lrmaxqinit_p02 = LRMaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta,
n_states=n_states, max_memory_size=max_mem, prior=0.2, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMaxQInit(Dmax=0.2)')
agents_pool = [rmax, lrmax, lrmax_p01, lrmax_p02, maxqinit, lrmaxqinit, lrmaxqinit_p01, lrmaxqinit_p02]
# Run
run_agents_lifelong(agents_pool, env_distribution, n_instances=3, n_tasks=p['n_tasks'], n_episodes=p['n_episodes'],
n_steps=p['n_steps'],
reset_at_terminal=False, open_plot=False, plot_title=True, do_run=True, do_plot=True,
parallel_run=True, n_processes=None)
def experiment():
n_env = 5
env_distribution = make_env_distribution(env_class='maze-mono-goal', env_name='maze-mono-goal', n_env=n_env, gamma=GAMMA)
actions = env_distribution.get_actions()
p_min = 1. / float(n_env)
delta = .1
m = 100
max_mem = 10
rmax = RMax(actions=actions, gamma=GAMMA, count_threshold=m)
rmax_q = MaxQInit(actions=actions, gamma=GAMMA, count_threshold=m, min_sampling_probability=p_min, delta=delta)
lrmax1 = LRMax(actions=actions, gamma=GAMMA, count_threshold=m, max_memory_size=max_mem, prior=1.)
lrmax05 = LRMax(actions=actions, gamma=GAMMA, count_threshold=m, max_memory_size=max_mem, prior=0.5)
lrmax02 = LRMax(actions=actions, gamma=GAMMA, count_threshold=m, max_memory_size=max_mem, prior=0.2)
lrmax_learn = LRMax(
actions=actions, gamma=GAMMA, count_threshold=m, max_memory_size=max_mem, prior=None,
min_sampling_probability=p_min, delta=delta
)
agents_pool = [rmax, lrmax1, lrmax05, lrmax02, lrmax_learn, rmax_q]
run_agents_lifelong(
agents_pool, env_distribution, samples=20, episodes=100, steps=1000, reset_at_terminal=False,
open_plot=True, cumulative_plot=False, is_tracked_value_discounted=True, plot_only=False, plot_title=False
)
def experiment():
n_env = 5
env_distribution = make_env_distribution(env_class='corridor',
n_env=n_env,
gamma=GAMMA,
w=20,
h=1)
actions = env_distribution.get_actions()
p_min = 1. / float(n_env)
delta = .1
m = 1
max_mem = 2
rmax = RMax(actions=actions, gamma=GAMMA, count_threshold=m)
rmax_q = MaxQInit(actions=actions,
gamma=GAMMA,
count_threshold=m,
min_sampling_probability=p_min,
delta=delta)
lrmax0_2 = LRMaxCT(actions=actions,
gamma=GAMMA,
count_threshold=m,
max_memory_size=max_mem,
prior=0.2)
lrmax0_6 = LRMaxCT(actions=actions,
gamma=GAMMA,
count_threshold=m,
max_memory_size=max_mem,
prior=0.6)
lrmax1_0 = LRMaxCT(actions=actions,
gamma=GAMMA,
count_threshold=m,
max_memory_size=max_mem,
prior=1.0)
lrmax_learn = LRMaxCT(actions=actions,
gamma=GAMMA,
count_threshold=m,
max_memory_size=max_mem,
prior=None,
min_sampling_probability=p_min,
delta=delta)
agents_pool = [rmax, lrmax1_0, lrmax0_6, lrmax0_2, lrmax_learn, rmax_q]
run_agents_lifelong(agents_pool,
env_distribution,
samples=20,
episodes=20,
steps=10,
reset_at_terminal=False,
open_plot=True,
cumulative_plot=False,
is_tracked_value_discounted=False,
plot_only=False,
plot_title=False)
def __init__(self,
actions,
gamma=.9,
r_max=1.,
v_max=None,
deduce_v_max=True,
n_known=None,
deduce_n_known=True,
epsilon_q=0.1,
epsilon_m=None,
delta=None,
n_states=None,
name="ExpRMax",
path='results/'):
RMax.__init__(self,
actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=deduce_v_max,
n_known=n_known,
deduce_n_known=deduce_n_known,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
name=name)
# Recorded variables
self.discounted_return = 0.
self.total_return = 0.
self.n_time_steps = 0 # nb of time steps
self.update_time_steps = [] # time steps where a model update occurred
self.path = path
self.instance_number = 0
self.run_number = 0
def experiment():
# Parameters
gamma = .9
env_distribution = make_env_distribution(env_class='deterministic-super-tight',
env_name='deterministic-super-tight-bignknown',
gamma=gamma)
actions = env_distribution.get_actions()
n_known = 100
p_min = 1. / 3.
epsilon_q = .01
epsilon_m = .01
delta = .1
r_max = 1.
v_max = 1.
n_states = 4
max_mem = 9
# Agents
rmax = RMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, name='RMax')
lrmax = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=None, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax')
lrmaxprior = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=0.1, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax(Dmax=0.1)')
maxqinit = MaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
min_sampling_probability=p_min, name='MaxQInit')
lrmaxqinit = LRMaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta,
n_states=n_states, max_memory_size=max_mem, prior=None, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMaxQInit')
lrmaxqinitprior = LRMaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta,
n_states=n_states, max_memory_size=max_mem, prior=0.1, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMaxQInit(Dmax=0.1)')
agents_pool = [rmax, lrmax, lrmaxprior, maxqinit, lrmaxqinit, lrmaxqinitprior]
# Run
run_agents_lifelong(agents_pool, env_distribution, n_instances=1, n_tasks=100, n_episodes=200, n_steps=100,
reset_at_terminal=False, open_plot=False, plot_title=True, do_run=False, do_plot=True,
parallel_run=True, n_processes=None)
def main():
# Setup MDP.
w = 6
h = 6
mdp = GridWorld(width=w,
height=h,
init_loc=(1, 1),
goal_locs=[(6, 6)],
slip_prob=.1)
# Setup Agents.
rand_agent = RandomAgent(actions=mdp.get_actions())
ql_agent = QLearningAgent(actions=mdp.get_actions())
# Compute number of samples for R-MAX to achieve epsilon optimal behavior with high probability (1 - delta)
compute_n_samples = False
if compute_n_samples:
epsilon = .1
delta = .05
m_r = np.log(2. / delta) / (2. * epsilon**2)
m_t = 2. * (np.log(2**(float(w * h)) - 2.) - np.log(delta)) / (epsilon
**2)
n_samples = int(max(m_r, m_t))
else:
n_samples = 30
simple_rl_rmax_agent = RMaxAgent(actions=mdp.get_actions(),
gamma=.9,
horizon=3,
s_a_threshold=n_samples,
name='SimpleRL-R-MAX')
rmax_agent = RMax(actions=mdp.get_actions(),
gamma=.9,
count_threshold=n_samples)
# Run experiment and make plot.
run_agents_on_mdp([rand_agent, ql_agent, rmax_agent, simple_rl_rmax_agent],
mdp,
instances=5,
episodes=100,
steps=20,
reset_at_terminal=True,
verbose=False)
def experiment():
# Parameters
gamma = .9
env_distribution = make_env_distribution(env_class='stochastic-tight', env_name='stochastic-tight', gamma=gamma)
actions = env_distribution.get_actions()
n_known = 10
p_min = 1. / 7. # There are seven possible MDPs
epsilon_q = .1
epsilon_m = .01
delta = .1
r_max = 1.
v_max = 1.
n_states = 4
max_mem = 10
# Agents
rmax = RMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, name='RMax')
lrmax = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=None, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax')
lrmaxprior = LRMax(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
max_memory_size=max_mem, prior=0.2, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMax(Dmax=0.2)')
maxqinit = MaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta, n_states=n_states,
min_sampling_probability=p_min, name='MaxQInit')
lrmaxqinit = LRMaxQInit(actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=False, n_known=n_known,
deduce_n_known=False, epsilon_q=epsilon_q, epsilon_m=epsilon_m, delta=delta,
n_states=n_states, max_memory_size=max_mem, prior=None, estimate_distances_online=True,
min_sampling_probability=p_min, name='LRMaxQInit')
agents_pool = [rmax, lrmax, lrmaxprior, maxqinit] # , lrmaxqinit]
# Run
run_agents_lifelong(agents_pool, env_distribution, n_instances=5, n_tasks=50, n_episodes=50, n_steps=100,
reset_at_terminal=False, plot_only=False, open_plot=True, plot_title=True)
def example():
n_env = 4
env_distribution = make_env_distribution(env_class='test',
n_env=n_env,
gamma=GAMMA,
w=60,
h=20)
actions = env_distribution.get_actions()
m = 1 # Count threshold
max_mem = None
p_min = 1. / float(n_env)
delta = 0.99
lrmax = LRMax(actions=actions,
gamma=GAMMA,
count_threshold=m,
max_memory_size=max_mem,
prior=None,
min_sampling_probability=p_min,
delta=delta)
rmax_max_q_init = MaxQInit(actions=actions,
gamma=GAMMA,
count_threshold=m,
min_sampling_probability=p_min,
delta=delta)
rmax = RMax(actions=actions, gamma=GAMMA, count_threshold=m)
run_agents_lifelong([rmax_max_q_init, lrmax, rmax],
env_distribution,
samples=10,
episodes=10,
steps=100,
reset_at_terminal=False,
open_plot=True,
cumulative_plot=False,
is_tracked_value_discounted=True,
plot_only=False)
def experiment():
# Parameters
gamma = .9
n_env = 5
n_states = 20
env_distribution = make_env_distribution(env_class='corridor',
n_env=n_env,
gamma=gamma,
w=n_states,
h=1)
actions = env_distribution.get_actions()
n_known = 1
p_min = 1. / float(n_env)
r_max = 1.
v_max = 10.
epsilon_q = .01
epsilon_m = .01
delta = .1
max_mem = 1
# Agents
rmax = RMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
name='RMax')
lrmax = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=None,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMax')
lrmaxprior02 = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.2,
estimate_distances_online=False,
min_sampling_probability=p_min,
name='LRMax(0.2)')
maxqinit = MaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
min_sampling_probability=p_min,
name='MaxQInit')
lrmaxqinit = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=None,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit')
lrmaxqinitprior02 = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.2,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit(0.2)')
agents_pool = [
rmax, lrmax, lrmaxprior02, maxqinit, lrmaxqinit, lrmaxqinitprior02
]
# Run
run_agents_lifelong(agents_pool,
env_distribution,
name_identifier=None,
n_instances=1,
n_tasks=20,
n_episodes=20,
n_steps=11,
reset_at_terminal=False,
do_run=False,
do_plot=True,
open_plot=False,
episodes_moving_average=False,
episodes_ma_width=10,
tasks_moving_average=False,
tasks_ma_width=10,
latex_rendering=True,
plot_title=False)
def __init__(self,
actions,
gamma=.9,
r_max=1.,
v_max=None,
deduce_v_max=True,
n_known=None,
deduce_n_known=True,
epsilon_q=0.1,
epsilon_m=None,
delta=None,
n_states=None,
max_memory_size=None,
prior=None,
estimate_distances_online=True,
min_sampling_probability=.1,
name="LRMax"):
"""
:param actions: action space of the environment
:param gamma: (float) discount factor
:param r_max: (float) known upper-bound on the reward function
:param v_max: (float) known upper-bound on the value function
:param deduce_v_max: (bool) set to True to deduce v_max from r_max
:param n_known: (int) count after which a state-action pair is considered known
:param deduce_n_known: (bool) set to True to deduce n_known from (delta, n_states, epsilon_m)
(only set n_known if delta and epsilon are not defined)
:param epsilon_q: (float) precision of value iteration algorithm for Q-value computation
:param epsilon_m: (float) precision of the learned models in L1 norm
:param delta: (float) models are learned epsilon_m-closely with probability at least 1 - delta
:param n_states: (int) number of states
:param max_memory_size: (int) maximum number of saved models (infinity if None)
:param prior: (float) prior knowledge of maximum model's distance
:param estimate_distances_online: (bool) set to True for online estimation of a tighter upper-bound for the
model pseudo-distances. The estimation is valid with high probability.
:param min_sampling_probability: (float) minimum sampling probability of an environment
:param name: (str)
"""
self.name = name
RMax.__init__(self,
actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=deduce_v_max,
n_known=n_known,
deduce_n_known=deduce_n_known,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
name=name)
# Lifelong Learning memories
self.max_memory_size = max_memory_size
self.U_memory = []
self.R_memory = []
self.T_memory = []
self.SA_memory = defaultdict(lambda: defaultdict(lambda: False))
self.U_lip = []
self.b = self.epsilon_m * (1. + self.gamma * self.v_max)
# Prior knowledge on maximum model distance
prior_max = self.r_max + self.gamma * 2. * self.v_max
self.prior = prior_max if prior is None else min(prior, prior_max)
self.prior = round(self.prior, 2)
# Online distances estimation
self.estimate_distances_online = estimate_distances_online
self.min_sampling_probability = min_sampling_probability
self.D = defaultdict(lambda: defaultdict(lambda: prior_max)
) # Dictionary of distances (high probability)
self.n_samples_high_confidence = compute_n_samples_high_confidence(
min_sampling_probability, delta)
self.update_upper_bound()
def experiment():
# Parameters
gamma = .9
n_env = 5
w, h = 20, 20
n_states = w * h
env_distribution = make_env_distribution(
env_class='grid-world',
env_name='grid-world-two-goals-large',
n_env=n_env,
gamma=gamma,
w=w,
h=h)
actions = env_distribution.get_actions()
n_known = 1
p_min = 1. / float(n_env)
r_max = 1.
v_max = 10.
epsilon_q = .01
epsilon_m = .01
delta = .1
max_mem = 1
# Agents
rmax = RMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
name='RMax')
lrmax = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=None,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMax')
lrmaxprior02 = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.2,
estimate_distances_online=False,
min_sampling_probability=p_min,
name='LRMax(Dmax0.2)')
maxqinit = MaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
min_sampling_probability=p_min,
name='MaxQInit')
lrmaxqinit = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=None,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit')
lrmaxqinitprior02 = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.2,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit(Dmax0.2)')
agents_pool = [
rmax, lrmax, lrmaxprior02, maxqinit, lrmaxqinit, lrmaxqinitprior02
]
# Run
run_agents_lifelong(agents_pool,
env_distribution,
name_identifier=None,
n_instances=1,
n_tasks=100,
n_episodes=100,
n_steps=13,
reset_at_terminal=False,
open_plot=False,
plot_title=True,
do_run=True,
do_plot=True,
parallel_run=True,
n_processes=None)
def experiment(p, name):
# Parameters
gamma = .9
n_env = 5
size = p['size']
env_distribution = make_env_distribution(env_class='tight',
n_env=n_env,
gamma=gamma,
env_name=name,
version=p['version'],
w=size,
h=size,
stochastic=p['stochastic'],
verbose=False)
actions = env_distribution.get_actions()
n_known = p['n_known']
p_min = 1. / n_env
epsilon_q = .01
epsilon_m = .01
delta = .1
r_max = 1.
v_max = 10.
n_states = 4
max_mem = 1
# Agents
rmax = RMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
name='RMax')
lrmax = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=None,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMax')
lrmax_p01 = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.1,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMax(0.1)')
lrmax_p015 = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.15,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMax(0.15)')
lrmax_p02 = LRMax(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.2,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMax(0.2)')
maxqinit = MaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
min_sampling_probability=p_min,
name='MaxQInit')
lrmaxqinit = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=None,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit')
lrmaxqinit_p01 = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.1,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit(0.1)')
lrmaxqinit_p015 = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.15,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit(0.15)')
lrmaxqinit_p02 = LRMaxQInit(actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=False,
n_known=n_known,
deduce_n_known=False,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_mem,
prior=0.2,
estimate_distances_online=True,
min_sampling_probability=p_min,
name='LRMaxQInit(0.2)')
# agents_pool = [rmax, lrmax, lrmax_p01, lrmax_p015, lrmax_p02, maxqinit, lrmaxqinit, lrmaxqinit_p01, lrmaxqinit_p015, lrmaxqinit_p02]
agents_pool = [
rmax, lrmax, lrmax_p02, lrmax_p01, maxqinit, lrmaxqinit, lrmaxqinit_p01
]
# Run
run_agents_lifelong(agents_pool,
env_distribution,
n_instances=2,
n_tasks=p['n_tasks'],
n_episodes=p['n_episodes'],
n_steps=p['n_steps'],
reset_at_terminal=False,
open_plot=False,
plot_title=False,
plot_legend=2,
do_run=True,
do_plot=True,
parallel_run=True,
n_processes=None,
episodes_moving_average=True,
episodes_ma_width=100,
tasks_moving_average=False,
latex_rendering=True)
