def reward_tabular_normalized(policy, task, tol=1e-4):
'''
compute the expected reward / reward by value iteration
averaged over states.
'''
gtV = compute_tabular_value(task, tol) # ground truth values by value iteration.
V = reward_tabular(policy, task, tol)
return V / gtV
def expected_reward_tabular_normalized(policy, task, tol=1e-4):
'''
compute the expected reward / reward by value iteration
averaged over states.
'''
gtV = compute_tabular_value(task, tol) # ground truth values by value iteration.
V = reward_tabular(policy, task, tol)
rewards = [V[state] / gtV[state] for state in task.get_valid_states()]
return np.mean(rewards)
def reward_tabular_normalized_fix_start(policy, task, tol=1e-4):
'''
compute the expected reward / reward by value iteration
averaged over states.
'''
states = [task.start_state]
gtV = compute_tabular_value(task, tol) # ground truth values by value iteration.
V = reward_tabular(policy, task, tol)
rewards = {state: V[state] / gtV[state] for state in task.get_valid_states()}
return np.mean([rewards[state] for state in states])