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agent.py
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"""This module contains the class that defines the interaction between
different modules that govern agent's behavior.
"""
import numpy as np
from perception import HierarchicalPerception
from misc import ln, softmax
import scipy.special as scs
class BayesianPlanner(object):
def __init__(self, perception, action_selection, policies,
prior_states = None, prior_policies = None,
prior_context = None,
learn_habit = False,
learn_rew = False,
trials = 1, T = 10, number_of_states = 6,
number_of_rewards = 2,
number_of_policies = 10):
#set the modules of the agent
self.perception = perception
self.action_selection = action_selection
#set parameters of the agent
self.nh = number_of_states #number of states
self.npi = number_of_policies #number of policies
self.nr = number_of_rewards
self.T = T
if policies is not None:
self.policies = policies
else:
#make action sequences for each policy
self.policies = np.eye(self.npi, dtype = int)
self.possible_polcies = self.policies.copy()
self.actions = np.unique(self.policies)
self.na = len(self.actions)
if prior_states is not None:
self.prior_states = prior_states
else:
self.prior_states = np.ones(self.nh)
self.prior_states /= self.prior_states.sum()
if prior_context is not None:
self.prior_context = prior_context
self.nc = prior_context.shape[0]
else:
self.prior_context = np.ones(1)
self.nc = 1
if prior_policies is not None:
self.prior_policies = np.tile(prior_policies, (1,self.nc)).T
else:
self.prior_policies = np.ones((self.npi,self.nc))/self.npi
self.learn_habit = learn_habit
self.learn_rew = learn_rew
#set various data structures
self.actions = np.zeros((trials, T), dtype = int)
self.posterior_states = np.zeros((trials, T, self.nh, T, self.npi, self.nc))
self.posterior_policies = np.zeros((trials, T, self.npi, self.nc))
self.posterior_dirichlet_pol = np.zeros((trials, self.npi, self.nc))
self.posterior_dirichlet_rew = np.zeros((trials, T, self.nr, self.nh, self.nc))
self.observations = np.zeros((trials, T), dtype = int)
self.rewards = np.zeros((trials, T), dtype = int)
self.posterior_context = np.ones((trials, T, self.nc))
self.posterior_context[:,:,:] = self.prior_context[np.newaxis,np.newaxis,:]
self.likelihood = np.zeros((trials, T, self.npi, self.nc))
self.prior_policies = np.zeros((trials, self.npi, self.nc))
self.prior_policies[:] = prior_policies[np.newaxis,:,:]
self.posterior_actions = np.zeros((trials, T-1, self.na))
self.posterior_rewards = np.zeros((trials, T, self.nr))
self.log_probability = 0
if hasattr(self.perception, 'generative_model_context'):
self.context_obs = np.zeros(trials, dtype=int)
def reset(self, params, fixed):
self.actions[:] = 0
self.posterior_states[:] = 0
self.posterior_policies[:] = 0
self.posterior_dirichlet_pol[:] = 0
self.posterior_dirichlet_rew[:] =0
self.observations[:] = 0
self.rewards[:] = 0
self.posterior_context[:,:,:] = self.prior_context[np.newaxis,np.newaxis,:]
self.likelihood[:] = 0
self.posterior_actions[:] = 0
self.posterior_rewards[:] = 0
self.log_probability = 0
self.perception.reset(params, fixed)
def update_beliefs(self, tau, t, observation, reward, response, context=None):
self.observations[tau,t] = observation
self.rewards[tau,t] = reward
if context is not None:
self.context_obs[tau] = context
if t == 0:
self.possible_polcies = np.arange(0,self.npi,1).astype(np.int32)
else:
possible_policies = np.where(self.policies[:,t-1]==response)[0]
self.possible_polcies = np.intersect1d(self.possible_polcies, possible_policies)
self.log_probability += ln(self.posterior_actions[tau,t-1,response])
self.posterior_states[tau, t] = self.perception.update_beliefs_states(
tau, t,
observation,
reward,
self.policies,
self.possible_polcies)
#update beliefs about policies
self.posterior_policies[tau, t], self.likelihood[tau,t] = self.perception.update_beliefs_policies(tau, t)
if tau == 0:
prior_context = self.prior_context
else: #elif t == 0:
prior_context = np.dot(self.perception.transition_matrix_context, self.posterior_context[tau-1, -1]).reshape((self.nc))
# else:
# prior_context = np.dot(self.perception.transition_matrix_context, self.posterior_context[tau, t-1])
# check here what to do with the greater and equal sign
if self.nc>1 and t>=0:
if hasattr(self, 'context_obs'):
c_obs = self.context_obs[tau]
else:
c_obs = None
self.posterior_context[tau, t] = \
self.perception.update_beliefs_context(tau, t, \
reward, \
self.posterior_states[tau, t], \
self.posterior_policies[tau, t], \
prior_context, \
self.policies,\
context=c_obs)
elif self.nc>1 and t==0:
self.posterior_context[tau, t] = prior_context
else:
self.posterior_context[tau,t] = 1
# print(tau,t)
# print("prior", prior_context)
# print("post", self.posterior_context[tau, t])
if t < self.T-1:
post_pol = np.dot(self.posterior_policies[tau, t], self.posterior_context[tau, t])
self.posterior_actions[tau, t] = self.estimate_action_probability(tau, t, post_pol)
if t == self.T-1 and self.learn_habit:
self.posterior_dirichlet_pol[tau], self.prior_policies[tau] = self.perception.update_beliefs_dirichlet_pol_params(tau, t, \
self.posterior_policies[tau,t], \
self.posterior_context[tau,t])
if False:
self.posterior_rewards[tau, t-1] = np.einsum('rsc,spc,pc,c->r',
self.perception.generative_model_rewards,
self.posterior_states[tau,t,:,t],
self.posterior_policies[tau,t],
self.posterior_context[tau,t])
#if reward > 0:
# check later if stuff still works!
if self.learn_rew:# and t>0:#==self.T-1:
self.posterior_dirichlet_rew[tau,t] = self.perception.update_beliefs_dirichlet_rew_params(tau, t, \
reward, \
self.posterior_states[tau, t], \
self.posterior_policies[tau, t], \
self.posterior_context[tau,t])
def generate_response(self, tau, t):
#get response probability
posterior_states = self.posterior_states[tau, t]
posterior_policies = np.einsum('pc,c->p', self.posterior_policies[tau, t], self.posterior_context[tau, 0])
posterior_policies /= posterior_policies.sum()
avg_likelihood = np.einsum('pc,c->p', self.likelihood[tau,t], self.posterior_context[tau, 0])
avg_likelihood /= avg_likelihood.sum()
prior = np.einsum('pc,c->p', self.prior_policies[tau-1], self.posterior_context[tau, 0])
prior /= prior.sum()
#print(self.posterior_context[tau, t])
non_zero = posterior_policies > 0
controls = self.policies[:, t]#[non_zero]
actions = np.unique(controls)
# posterior_policies = posterior_policies[non_zero]
# avg_likelihood = avg_likelihood[non_zero]
# prior = prior[non_zero]
self.actions[tau, t] = self.action_selection.select_desired_action(tau,
t, posterior_policies, controls, avg_likelihood, prior)
return self.actions[tau, t]
def estimate_action_probability(self, tau, t, posterior_policies):
#estimate action probability
control_prob = np.zeros(self.na)
for a in range(self.na):
control_prob[a] = posterior_policies[self.policies[:,t] == a].sum()
return control_prob
class BayesianPlanner_old(object):
def __init__(self, perception, action_selection, policies,
prior_states = None, prior_policies = None,
prior_context = None,
learn_habit = False,
learn_rew = False,
trials = 1, T = 10, number_of_states = 6,
number_of_rewards = 2,
number_of_policies = 10):
#set the modules of the agent
self.perception = perception
self.action_selection = action_selection
#set parameters of the agent
self.nh = number_of_states #number of states
self.npi = number_of_policies #number of policies
self.nr = number_of_rewards
self.T = T
if policies is not None:
self.policies = policies
else:
#make action sequences for each policy
self.policies = np.eye(self.npi, dtype = int)
self.possible_polcies = self.policies.copy()
self.actions = np.unique(self.policies)
self.na = len(self.actions)
if prior_states is not None:
self.prior_states = prior_states
else:
self.prior_states = np.ones(self.nh)
self.prior_states /= self.prior_states.sum()
if prior_context is not None:
self.prior_context = prior_context
self.nc = prior_context.shape[0]
else:
self.prior_context = np.ones(1)
self.nc = 1
if prior_policies is not None:
self.prior_policies = np.tile(prior_policies, (1,self.nc)).T
else:
self.prior_policies = np.ones((self.npi,self.nc))/self.npi
self.learn_habit = learn_habit
self.learn_rew = learn_rew
#set various data structures
self.actions = np.zeros((trials, T), dtype = int)
self.posterior_states = np.zeros((trials, T, self.nh, T, self.npi, self.nc))
self.posterior_policies = np.zeros((trials, T, self.npi, self.nc))
self.posterior_dirichlet_pol = np.zeros((trials, self.npi, self.nc))
self.posterior_dirichlet_rew = np.zeros((trials, T, self.nr, self.nh, self.nc))
self.observations = np.zeros((trials, T), dtype = int)
self.rewards = np.zeros((trials, T), dtype = int)
self.posterior_context = np.ones((trials, T, self.nc))
self.posterior_context[:,:,:] = self.prior_context[np.newaxis,np.newaxis,:]
self.likelihood = np.zeros((trials, T, self.npi, self.nc))
self.prior_policies = np.zeros((trials, self.npi, self.nc))
self.prior_policies[:] = prior_policies[np.newaxis,:,:]
self.posterior_actions = np.zeros((trials, T-1, self.na))
self.posterior_rewards = np.zeros((trials, T, self.nr))
self.log_probability = 0
def reset(self, params, fixed):
self.actions[:] = 0
self.posterior_states[:] = 0
self.posterior_policies[:] = 0
self.posterior_dirichlet_pol[:] = 0
self.posterior_dirichlet_rew[:] =0
self.observations[:] = 0
self.rewards[:] = 0
self.posterior_context[:,:,:] = self.prior_context[np.newaxis,np.newaxis,:]
self.likelihood[:] = 0
self.posterior_actions[:] = 0
self.posterior_rewards[:] = 0
self.log_probability = 0
self.perception.reset(params, fixed)
def update_beliefs(self, tau, t, observation, reward, response):
self.observations[tau,t] = observation
self.rewards[tau,t] = reward
if t == 0:
self.possible_polcies = np.arange(0,self.npi,1).astype(np.int32)
else:
possible_policies = np.where(self.policies[:,t-1]==response)[0]
self.possible_polcies = np.intersect1d(self.possible_polcies, possible_policies)
self.log_probability += ln(self.posterior_actions[tau,t-1,response])
self.posterior_states[tau, t] = self.perception.update_beliefs_states(
tau, t,
observation,
reward,
self.policies,
self.possible_polcies)
#update beliefs about policies
self.posterior_policies[tau, t], self.likelihood[tau,t] = self.perception.update_beliefs_policies(tau, t)
if tau == 0:
prior_context = self.prior_context
else: #elif t == 0:
prior_context = np.dot(self.perception.transition_matrix_context, self.posterior_context[tau-1, -1]).reshape((self.nc))
# else:
# prior_context = np.dot(self.perception.transition_matrix_context, self.posterior_context[tau, t-1])
if self.nc>1 and t>0:
self.posterior_context[tau, t] = \
self.perception.update_beliefs_context(tau, t, \
reward, \
self.posterior_states[tau, t], \
self.posterior_policies[tau, t], \
prior_context, \
self.policies)
elif self.nc>1 and t==0:
self.posterior_context[tau, t] = prior_context
else:
self.posterior_context[tau,t] = 1
# print(tau,t)
# print("prior", prior_context)
# print("post", self.posterior_context[tau, t])
if t < self.T-1:
post_pol = np.dot(self.posterior_policies[tau, t], self.posterior_context[tau, t])
self.posterior_actions[tau, t] = self.estimate_action_probability(tau, t, post_pol)
if t == self.T-1 and self.learn_habit:
self.posterior_dirichlet_pol[tau], self.prior_policies[tau] = self.perception.update_beliefs_dirichlet_pol_params(tau, t, \
self.posterior_policies[tau,t], \
self.posterior_context[tau,t])
if False:
self.posterior_rewards[tau, t-1] = np.einsum('rsc,spc,pc,c->r',
self.perception.generative_model_rewards,
self.posterior_states[tau,t,:,t],
self.posterior_policies[tau,t],
self.posterior_context[tau,t])
#if reward > 0:
if self.learn_rew:
self.posterior_dirichlet_rew[tau,t] = self.perception.update_beliefs_dirichlet_rew_params(tau, t, \
reward, \
self.posterior_states[tau, t], \
self.posterior_policies[tau, t], \
self.posterior_context[tau,t])
def generate_response(self, tau, t):
#get response probability
posterior_states = self.posterior_states[tau, t]
posterior_policies = np.einsum('pc,c->p', self.posterior_policies[tau, t], self.posterior_context[tau, 0])
posterior_policies /= posterior_policies.sum()
avg_likelihood = np.einsum('pc,c->p', self.likelihood[tau,t], self.posterior_context[tau, 0])
avg_likelihood /= avg_likelihood.sum()
prior = np.einsum('pc,c->p', self.prior_policies[tau-1], self.posterior_context[tau, 0])
prior /= prior.sum()
#print(self.posterior_context[tau, t])
non_zero = posterior_policies > 0
controls = self.policies[:, t]#[non_zero]
actions = np.unique(controls)
# posterior_policies = posterior_policies[non_zero]
# avg_likelihood = avg_likelihood[non_zero]
# prior = prior[non_zero]
self.actions[tau, t] = self.action_selection.select_desired_action(tau,
t, posterior_policies, controls, avg_likelihood, prior)
return self.actions[tau, t]
def estimate_action_probability(self, tau, t, posterior_policies):
#estimate action probability
control_prob = np.zeros(self.na)
for a in range(self.na):
control_prob[a] = posterior_policies[self.policies[:,t] == a].sum()
return control_prob