pi_star_perf = spibb.policy_evaluation_exact(pi_star.pi, r_reshaped, current_proba, gamma)[0][0]
print("pi_star_perf: " + str(pi_star_perf))
# Place to save the results
filename = 'results/' + expname + '/results_' + str(index)
results = []
if not os.path.isdir('results'):
os.mkdir('results')
if not os.path.isdir('results/' + expname):
os.mkdir('results/' + expname)
while True:
for nb_trajectories in nb_trajectories_list:
# Generate trajectories, both stored as trajectories and (s,a,s',r) transition samples
trajectories, batch_traj = spibb_utils.generate_batch(nb_trajectories, garnet, pi_behavioural)
spibb_utils.prt("GENERATED A DATASET OF " + str(nb_trajectories) + " TRAJECTORIES")
# Compute the maximal likelihood model for transitions and rewards.
# NB: the true reward function can be used for ease of implementation since it is not stochastic in our environment.
# One should compute it from the samples when it is stochastic.
model = modelTransitions.ModelTransitions(batch_traj, nb_states, nb_actions)
reward_model = spibb_utils.get_reward_model(model.transitions, reward_current)
# Computes the RL policy
rl = spibb.spibb(gamma, nb_states, nb_actions, pi_b, mask_0, model.transitions, reward_model, 'default')
rl.fit()
# Evaluates the RL policy performance
perfrl = spibb.policy_evaluation_exact(rl.pi, r_reshaped, current_proba, gamma)[0][0]
print("perf RL: " + str(perfrl))
true_rl.pi, r_reshaped, current_proba, gamma)[0][0]
print("Optimal perf in easter egg environment:\t\t\t" +
str(pi_star_perf))
pi_b_perf = spibb.policy_evaluation_exact(pi_b, r_reshaped,
current_proba,
gamma)[0][0]
print("Baseline perf in easter egg environment:\t\t\t" +
str(pi_b_perf))
else:
easter_egg = None
r_reshaped = spibb_utils.get_reward_model(current_proba,
reward_current)
for nb_trajectories in nb_trajectories_list:
# Generate trajectories, both stored as trajectories and (s,a,s',r) transition samples
trajectories, batch_traj = spibb_utils.generate_batch(
nb_trajectories, garnet, pi_b, easter_egg)
print("GENERATED A DATASET OF " + str(nb_trajectories) +
" TRAJECTORIES")
# Compute the maximal likelihood model for transitions and rewards.
# NB: the true reward function can be used for ease of implementation since it is not stochastic in our environment.
# One should compute it from the samples when it is stochastic.
model = modelTransitions.ModelTransitions(batch_traj, nb_states,
nb_actions)
reward_model = spibb_utils.get_reward_model(
model.transitions, reward_current)
# Estimates the values of the baseline policy with a monte-carlo estimation from the batch data:
# q_pib_est = spibb_utils.compute_q_pib_est(gamma, nb_states, nb_actions, trajectories)
# Computes the RL policy
