def generate_batch_data(self, N, policy=None):
#print "[rl_tools]: Generating " + str(N) + " episodes of " + ("batch training data..." if policy is None else "evaluation data...")
#index = pandas.MultiIndex.from_tuples([(n, t) for n in range(N) for t in range(self.episode_length) ], names=['episode','time'])
#data = pandas.DataFrame(index=index, columns=self.data_columns)
f = lambda trash: self.simulate_episode(policy)
if 1:
raw_data = parallel.largeparmap(f, range(N))
else:
raw_data = map(f, range(N))
#for n in range(N):
# data.ix[n] = raw_data[n]
data = raw_data
return data
def best_policy(domain, data):
f = lambda pars: err_array(domain, pars, data)
pars0 = domain.true_pars
ml_start = leastsq(f, pars0)[0]
if 1:
f = lambda pars: [tuple(pars), mfmc_evaluation(policy_wrt_approx_model(domain, pars), data, domain.distance_fn, domain.initstate, domain.episode_length, domain.at_goal)]
pars = hill_climb(f, domain.optimization_pars, ml_start)
else:
f = lambda pars: [pars, mfmc_evaluation(policy_wrt_approx_model(domain, pars), data, domain.distance_fn, domain.initstate, domain.episode_length, domain.at_goal)]
raw_returns = parallel.largeparmap(f, domain.initial_par_search_space)
ind = np.argmax([raw[1] for raw in raw_returns])
pars = raw_returns[ind][0]
#print pars
dynamics = lambda s, u: domain.approx_dynamics(s, u, pars)
T = deterministic_continuous_to_discrete_model(dynamics, domain.state_centers, domain.action_centers, domain.at_goal)
states_to_actions, V = value_iteration(T, domain.state_centers, domain.reward, threshold=domain.value_iteration_threshold)
return discrete_policy(domain, states_to_actions)
