def arg_max(q_list):
max_idx_list = np.argwhere(q_list == np.amax(q_list))
max_idx_list = max_idx_list.flatten().tolist()
return random.choice(max_idx_list)
if __name__ == "__main__":
env = Env()
agent = QLearningAgent(actions=list(range(env.n_actions)))
for episode in range(1000):
state = env.reset()
while True:
# 게임 환경과 상태를 초기화
env.render()
# 현재 상태에 대한 행동 선택
action = agent.get_action(state)
# 행동을 취한 후 다음 상태, 보상 에피소드의 종료여부를 받아옴
next_state, reward, done = env.step(action)
# <s,a,r,s'>로 큐함수를 업데이트
agent.learn(state, action, reward, next_state)
state = next_state
# 모든 큐함수를 화면에 표시
env.print_value_all(agent.q_table)
if done:
break
env = Env()
agent = SARSAgent(actions=list(range(env.n_actions)))
for episode in range(1000):
# reset environment and initialize state
state = env.reset()
# get action of state from agent
action = agent.get_action(str(state))
while True:
env.render()
# take action and proceed one step in the environment
next_state, reward, done = env.step(action)
next_action = agent.get_action(str(next_state))
# with sample <s,a,r,s',a'>, agent learns new q function
agent.learn(str(state), action, reward, str(next_state), next_action)
state = next_state
action = next_action
# print q function of all states at screen
env.print_value_all(agent.q_table)
# if episode ends, then break
if done:
break
from environment import Env
from QLearning import QLearning
if __name__ == "__main__":
env = Env()
QL = QLearning(list(range(env.n_actions)))
for episode in range(1000):
state = env.reset()
while True:
env.render()
# take action and proceed one step in the environment
action = QL.get_action(str(state))
next_state, reward, done = env.step(action)
# with sample <s,a,r,s'>, agent learns new q function
QL.learn(str(state), action, reward, str(next_state))
state = next_state
env.print_value_all(QL.q_table)
# if episode ends, then break
if done:
break
max_index_list.append(index)
return random.choice(max_index_list)
if __name__ == "__main__":
env = Env()
agent = QLearningAgent(actions=list(range(env.n_actions)))
for episode in range(1000):
state = env.reset()
while True:
env.render()
# take action and proceed one step in the environment
action = agent.get_action(str(state))
agent.save_samples_for_print(state, action)
next_state, reward, done = env.step(action)
# with sample <s,a,r,s'>, agent learns new q function
agent.learn(str(state), action, reward, str(next_state))
state = next_state
env.print_value_all(agent.q_table, agent.samples_for_print)
# if episode ends, then break
if done:
agent.samples_for_print.clear()
break
env = Env()
env.reset()
# action space 출력
print(env.n_actions)
# 에이전트의 행동을 환경에 전달하고 정보 받아오기
next_state, reward, done = env.step(1)
print(next_state, reward, done)
# q 함수를 담을 default dict
q_table = defaultdict(lambda: [0.0, 0.0, 0.0, 0.0])
q_table[str([1, 0])] = [100., 100., 100., 100.]
print(q_table['blah blah'])
print(q_table)
# 환경에 q함수를 출력
env.print_value_all(q_table)
# 환경 업데이트
env.render()
time.sleep(3)
# 에이전트 연속 행동
for i in range(100):
actions = np.random.choice(4)
env.step(actions)
env.render()
env.reset()
env.destroy()
