def test_gridworld_q_learning():
np.random.seed(0)
N = 5
goal_pos = np.array([[N-1, N-1]])
human_pos = np.array([[N-1, 0]])
human_radius = 2
grid = np.ones((N, N), dtype=float) * -1
grid = construct_goal_reward(grid, goal_pos, 10)
grid = construct_human_radius_reward(grid, human_pos, human_radius, -10)
env = GridWorld(
dimensions=(N, N),
init_pos=(0, 0),
goal_pos=goal_pos,
reward_grid=grid,
human_pos=human_pos,
action_success_rate=0.8,
render=True,
)
mdp_algo = q_learning(env.transition, env.reward, gamma=0.99)
mdp_algo.run()
policy = StochasticGreedyPolicy(
env.action_space(), mdp_algo, env.transition)
# plot results
R = env.reward.reshape((N, N)).T
V = np.asarray(mdp_algo.V).reshape((N, N)).T
plot_grid_map(R, "Reward", cmap=plt.cm.Reds)
plot_grid_map(V, "Value Function", cmap=plt.cm.Blues)
plt.show()
obs, rew, done, info = env.reset()
while not done:
act = policy.get_action(obs)
obs, rew, done, info = env.step(act)
time.sleep(0.2)
env.close()
def test_gridworld_value_iteration():
np.random.seed(0)
N = 10
goal_pos = np.array([[N-1, N-1], [N-1, N-2]])
human_pos = np.array([[N//2, N//2], [N-1, 0]])
human_radius = 3
grid = np.zeros((N, N), dtype=float)
grid = construct_goal_reward(grid, goal_pos, 10)
grid = construct_human_radius_reward(grid, human_pos, human_radius, -10)
env = GridWorld(
dimensions=(N, N),
init_pos=(0, 0),
goal_pos=goal_pos,
reward_grid=grid,
human_pos=human_pos,
action_success_rate=1,
render=True,
)
mdp_algo = value_iteration(env.transition, env.reward, gamma=0.99)
policy = EpsGreedyPolicy(env.action_space(), mdp_algo)
# plot results
R = env.reward.reshape((N, N)).T
V = np.asarray(mdp_algo.V).reshape((N, N)).T
plot_grid_map(R, "Reward", cmap=plt.cm.Reds)
plot_grid_map(V, "Value Function", cmap=plt.cm.Blues)
plot_policy(policy, (N, N), "Policy", values=V, cmap=plt.cm.Blues)
plt.show()
obs, rew, done, info = env.reset()
while not done:
act = policy.get_action(obs)
obs, rew, done, info = env.step(act)
time.sleep(0.2)
env.close()
