np.random.seed(seed)
random.seed(seed)
#let's try out BIRL on a simpler version and see what happens
#first let's give a demo in the A version that doesn't have lava
mdp_env_A = mdp_worlds.lava_ird_simplified_a()
# mdp_env_A = mdp_worlds.lava_ambiguous_ird_fig2a()
u_sa_A = mdp.solve_mdp_lp(mdp_env_A)
print("mdp A")
print("Policy")
utils.print_policy_from_occupancies(u_sa_A, mdp_env_A)
print("reward")
utils.print_as_grid(mdp_env_A.r_s, mdp_env_A)
print("features")
utils.display_onehot_state_features(mdp_env_A)
#generate demo for Dylan's NeurIPS world
# demonstrations = utils.rollout_from_usa(51, 15, u_sa_A, mdp_env_A)
#generate demo for my simplified world
demonstrations = utils.rollout_from_usa(10, 100, u_sa_A, mdp_env_A)
print("demonstration")
print(demonstrations)
#Now let's run Bayesian IRL on this demo in this mdp with a placeholder feature to see what happens.
beta = 100.0
step_stdev = 0.1
print(demonstrations)
traj_demonstrations = [demonstrations]
beta = 10.0
step_stdev = 0.1
birl = bayesian_irl.BayesianIRL(mdp_env, beta, step_stdev, debug=False)
num_samples = 200
burn = 50
skip = 2
map_w, map_u, r_chain, u_chain = birl.sample_posterior(
demonstrations, num_samples)
print("map_weights", map_w)
map_r = np.dot(mdp_env.state_features, map_w)
utils.print_as_grid(map_r, mdp_env)
#print("Map policy")
#utils.print_policy_from_occupancies(map_u, mdp_env)
# print("chain")
# for r in r_chain:
# print(r)
worst_index = np.argmin(r_chain[:, 1])
print(r_chain[worst_index])
print(np.sum(r_chain[:, 1] < -0.82), "out of ", len(r_chain))
r_chain_burned = r_chain[burn::skip]
# print("chain after burn and skip")
# for r in r_chain_burned:
# print(r)
weights = utils.sample_l2_ball(num_features)
print("weights", weights)
gamma = 0.99
#let's look at all starting states for now
init_dist = np.ones(num_states) / num_states
# init_states = [10]
# for si in init_states:
# init_dist[si] = 1.0 / len(init_states)
#no terminal
term_states = []
mdp_env = mdp.FeaturizedGridMDP(num_rows, num_cols, state_features,
weights, gamma, init_dist, term_states)
return mdp_env
if __name__ == "__main__":
#mdp_env = machine_teaching_toy_featurized()
# mdp_env = lava_ambiguous_aaai18()
mdp_env = random_gridworld_corner_terminal(6, 6, 5)
print("features")
utils.display_onehot_state_features(mdp_env)
u_sa = mdp.solve_mdp_lp(mdp_env, debug=True)
print("optimal policy")
utils.print_policy_from_occupancies(u_sa, mdp_env)
print("optimal values")
v = mdp.get_state_values(u_sa, mdp_env)
utils.print_as_grid(v, mdp_env)
step_stdev = 0.2
burn = 50
skip = 5
num_samples = 200
mcmc_norm = "l2"
likelihood = "birl"
mdp_env = mdp_worlds.lava_ambiguous_corridor()
opt_sa = mdp.solve_mdp_lp(mdp_env)
print("Cliff world")
print("Optimal Policy")
utils.print_policy_from_occupancies(opt_sa, mdp_env)
print("reward")
utils.print_as_grid(mdp_env.r_s, mdp_env)
print("features")
utils.display_onehot_state_features(mdp_env)
init_demo_state = 1#mdp_env.num_cols * (mdp_env.num_rows - 1)
traj_demonstrations = []
demo_set = set()
for d in range(num_demos):
# np.random.seed(init_seed + d)
# random.seed(init_seed + d)
s = init_demo_state #mdp_env.init_states[0] # only one initial state
demo = utils.rollout_from_usa(s, demo_horizon, opt_sa, mdp_env)
print("demo", d, demo)
traj_demonstrations.append(demo)
for s_a in demo:
demo_set.add(s_a)
step_stdev = 0.2
burn = 500
skip = 5
num_samples = 2000
mcmc_norm = "l2"
likelihood = "birl"
mdp_env = mdp_worlds.lava_ambiguous_corridor()
opt_sa = mdp.solve_mdp_lp(mdp_env)
print("Cliff world")
print("Optimal Policy")
utils.print_policy_from_occupancies(opt_sa, mdp_env)
print("reward")
utils.print_as_grid(mdp_env.r_s, mdp_env)
print("features")
utils.display_onehot_state_features(mdp_env)
init_demo_state = 1#mdp_env.num_cols * (mdp_env.num_rows - 1)
traj_demonstrations = []
demo_set = set()
for d in range(num_demos):
# np.random.seed(init_seed + d)
# random.seed(init_seed + d)
s = init_demo_state #mdp_env.init_states[0] # only one initial state
demo = utils.rollout_from_usa(s, demo_horizon, opt_sa, mdp_env)
print("demo", d, demo)
traj_demonstrations.append(demo)
for s_a in demo:
demo_set.add(s_a)
num_features = 6
train_mdp = mdp_worlds.negative_sideeffects_goal(num_rows,
num_cols,
num_features,
unseen_feature=False)
train_mdp.set_reward_fn(np.array([-.1, -.6, -.1, -0.6, -2, 0]))
opt_sa_train = mdp.solve_mdp_lp(train_mdp)
print("===========================")
print("Training MDP with No Lava")
print("===========================")
print("Optimal Policy")
utils.print_policy_from_occupancies(opt_sa_train, train_mdp)
print("reward")
utils.print_as_grid(train_mdp.r_s, train_mdp)
print("features")
utils.display_onehot_state_features(train_mdp)
import numpy as np
np.random.randint(60)
init_demo_states = [0, 9, 90, 99] #mdp_env.num_cols * (mdp_env.num_rows - 1)
traj_demonstrations = []
demo_set = set()
for d in range(num_demos):
# np.random.seed(init_seed + d)
# random.seed(init_seed + d)
for s in init_demo_states:
#s = init_demo_state #mdp_env.init_states[0] # only one initial state
