agent = Agent(**cfg)
agent.load_dict()
for episode in range(300):
s0 = env.reset()
total_reward = 1
while True:
a0 = agent.act(s0)
s1, r1, done, _ = env.step(a0)
if done:
r1 = -1
agent.put(s0, a0, r1, s1)
if done:
break
total_reward += r1
s0 = s1
agent.learn()
agent.load_dict()
print("episode", episode, "total_reward", total_reward)
path = os.path.realpath(args.ckpt_path)
if not os.path.exists(path):
os.makedirs(path)
ckpt_name = path + "/dqn.ckpt"
save_checkpoint(agent.policy_net, ckpt_name)
position_bounds, # Position bounds
velocity_bounds # Velocity bounds
)
# Instanced Agent
agent = Agent(
policy, # NeuralNetwork class
model,
actions, # Actions array (after discretization)
episodes, # Max number of episodes
epoches, # Max number of epoches per episode
greed_factor # Greed factor
)
# Getting the result array of len(episodes) length
results = agent.learn()
# Success episodes
success_results = [x for x in results if x["state"][0] >= position_bounds[1]]
# Writing the log file
log_file = open("model.log", "w+")
log_file.write("Episodes: {0}\n".format(episodes))
log_file.write("Epoches: {0}\n".format(epoches))
log_file.write("Epsilon-Greedy: {0}\n".format(greed_factor))
log_file.write("\n------- {0} successful episodes -------\n\n".format(
len(success_results)))
for r in success_results:
log_file.write(str(r) + "\n")
