def train(env, config):
"""
"""
t0 = time.time()
save_models_path = str(config["locexp"])
memory = ReplayBuffer((8, ), (1, ), config["buffer_size"], config["seed"],
config["device"])
memory.load_memory(config["buffer_path"])
agent = Agent(state_size=8, action_size=4, config=config)
if config["idx"] < memory.idx:
memory.idx = config["idx"]
print("memory idx ", memory.idx)
for t in range(config["predicter_time_steps"]):
text = "Train Predicter {} \ {} time {} \r".format(
t, config["predicter_time_steps"], time_format(time.time() - t0))
print(text, end='')
agent.learn(memory)
if t % int(config["eval"]) == 0:
print(text)
agent.save(save_models_path + "/models/{}-".format(t))
#agent.test_predicter(memory)
agent.test_q_value(memory)
agent.eval_policy()
agent.eval_policy(True, 1)
def train(env, config):
"""
"""
t0 = time.time()
memory = ReplayBuffer((8,), (1,), config["expert_buffer_size"], config["device"])
memory.load_memory(config["buffer_path"])
agent = Agent(state_size=8, action_size=4, config=config)
memory.idx = config["idx"]
#for i in range(10):
# print("state", memory.obses[i])
# sys.exit()
print("memroy idx ",memory.idx)
if config["mode"] == "predict":
for t in range(config["predicter_time_steps"]):
text = "Train Predicter {} \ {} time {} \r".format(t, config["predicter_time_steps"], time_format(time.time() - t0))
print(text, end = '')
agent.learn_predicter(memory)
if t % 2000 == 0:
# agent.test_predicter(memory)
agent.save("pytorch_models-{trained_predicter}/")
return
if config["mode"] == "iql":
agent.test_predicter(memory)
for t in range(config["predicter_time_steps"]):
text = "Train Predicter {} \ {} time {} \r".format(t, config["predicter_time_steps"], time_format(time.time() - t0))
print(text, end = '')
agent.learn(memory)
if t % 100 == 0:
print(text)
agent.test_predicter(memory)
agent.test_q_value(memory)
# agent.test_policy()
if config["mode"] == "dqn":
print("mode dqn")
agent.dqn_train()
return
