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td.py
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td.py
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import os,sys
import numpy as np
class TemporalDifference:
def __init__(self,state_list,action_list):
self.states = state_list
self.actions = action_list
self.state_num = len(self.states)
self.action_num = len(self.actions)
self.Q = dict()
for s in self.states:
#self.Q[s] = np.random.random(self.action_num)
self.Q[s] = np.zeros(self.action_num)
def set_policy(self,learning_type):
self.pi = dict()
self.mu = dict()
if learning_type == 'sarsa':
for s in self.states:
self.pi[s] = np.random.random(self.action_num)
self.pi[s] = self.pi[s] / np.sum(self.pi[s])
elif learning_type == 'q-learning':
for s in self.states:
idx = np.random.randint(0,self.action_num,size=1)[0]
self.pi[s] = np.zeros(self.action_num)
self.pi[s][idx] = 1.0
self.mu[s] = np.random.random(self.action_num)
self.mu[s] = self.mu[s] / np.sum(self.mu[s])
def sarsa_learning(self,env,episode_num,epsilon,alpha,gamma,max_timestep,eval_interval):
ep_idx = 0
avg_ep_return_list = []
while ep_idx < episode_num:
if ep_idx % eval_interval == 0:
eval_ep = env.episode_generator(self.pi,max_timestep,True)
print("eval episode length:%d" %(len(eval_ep)/3))
c_avg_return = env.avg_return_per_episode(eval_ep)
avg_ep_return_list.append(c_avg_return)
print("assessing return:%f" %c_avg_return)
print "avg return list length:",len(avg_ep_return_list)
ep_idx += 1
env.c_state = env.getInitState()
env.next_state = env.c_state
n = 0
c_action_idx = np.random.choice(self.action_num, 1, p=self.pi[env.c_state])[0]
env.c_action = self.actions[c_action_idx]
#print "episode index:",ep_idx
#print "env termination:",env.isTerminated()
while not (env.isTerminated() or n >= max_timestep) :
env.c_state = env.next_state
env.c_action = self.actions[c_action_idx]
#print "policy:",self.pi
env.c_state,env.c_action,env.c_reward,env.next_state = env.oneStep_generator()
next_action_idx = np.random.choice(self.action_num,1,p=self.pi[env.next_state])[0]
self.Q[env.c_state][c_action_idx] += alpha * (env.c_reward + gamma * self.Q[env.next_state][next_action_idx] - self.Q[env.c_state][c_action_idx])
# --------policy update at same time---------#
c_best_action_idx = np.argmax(self.Q[env.c_state])
for action_idx in range(self.action_num):
if action_idx == c_best_action_idx:
self.pi[env.c_state][action_idx] = 1 - epsilon + epsilon / self.action_num
else:
self.pi[env.c_state][action_idx] = epsilon / self.action_num
c_action_idx = next_action_idx
n += 1
#print "n:",n
return avg_ep_return_list
def Q_learning(self,env,episode_num,epsilon,alpha,gamma,max_timestep,eval_interval):
ep_idx = 0
avg_ep_return_list = []
while ep_idx < episode_num:
if ep_idx % eval_interval == 0:
eval_ep = env.episode_generator(self.pi,max_timestep,True)
print("eval episode length:%d" %(len(eval_ep)/3))
c_avg_return = env.avg_return_per_episode(eval_ep)
avg_ep_return_list.append(c_avg_return)
print("assessing return:%f" %c_avg_return)
print "avg return list length:",len(avg_ep_return_list)
ep_idx += 1
env.c_state = env.getInitState()
env.next_state = env.c_state
n = 0
while n < max_timestep and not env.isTerminated():
env.c_state = env.next_state
c_action_idx = np.random.choice(self.action_num,1,p=self.mu[env.c_state])[0]
env.c_action = self.actions[c_action_idx]
env.c_state, env.c_action, env.c_reward, env.next_state = env.oneStep_generator()
#print "c_state:",env.c_state
#print "c_action:",env.c_action
#print "c_reward:",env.c_reward
#print "next_state:",env.next_state
#print "c_state mu:",self.mu[env.c_state]
self.Q[env.c_state][c_action_idx] += alpha * (
env.c_reward + gamma * np.amax(self.Q[env.next_state]) - self.Q[env.c_state][c_action_idx])
c_best_action_idx = np.argmax(self.Q[env.c_state])
#print "c_state Q:",self.Q[env.c_state]
#print "c_best_action_idx:",c_best_action_idx
for action_idx in range(self.action_num):
if action_idx == c_best_action_idx:
self.mu[env.c_state][action_idx] = 1 - epsilon + epsilon/self.action_num
else:
self.mu[env.c_state][action_idx] = epsilon/self.action_num
# --------policy update at same time---------#
for action_idx in range(self.action_num):
if action_idx == c_best_action_idx:
self.pi[env.c_state][action_idx] = 1.0
else:
self.pi[env.c_state][action_idx] = 0.0
n += 1
return avg_ep_return_list