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(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='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 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 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 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)
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)
