#env = CustomEnv(3, 6, "tcp://*:5556")
# Stable Baselines provides you with make_vec_env() helper
# which does exactly the previous steps for you:
# env = make_vec_env(env_id, n_envs=num_cpu, seed=0)
# Create log dir
log_dir = "Logs/Custom_env/"
os.makedirs(log_dir, exist_ok=True)
# Create the callback: check every 1000 steps
callback = SaveOnBestTrainingRewardCallback(check_freq=500,
log_dir=log_dir)
#env = Monitor(env, log_dir)
model = ACKTR(MlpPolicy, env, verbose=2)
#model.load("DQN_agent")
model.learn(total_timesteps=20000, callback=callback)
model.save("temp_agent")
a = input("Training completed")
obs = env.reset()
for _ in range(1000):
action, _states = model.predict(obs, deterministic=True)
probs = model.action_probability(obs)
obs, rewards, dones, info = env.step(action)
print("Observation:", obs, rewards, probs)
results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS,
"Lane Manager")
plt.show()
set_global_seeds(seed)
return _init
if __name__ == '__main__':
env_id = "CartPole-v1"
num_cpu = 4 # Number of processes to use
# Create the vectorized environment
#env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])
#env = gym.make(env_id)
env = CustomEnv(3, 6, "tcp://*:5556")
# Stable Baselines provides you with make_vec_env() helper
# which does exactly the previous steps for you:
# env = make_vec_env(env_id, n_envs=num_cpu, seed=0)
# Create log dir
log_dir = "Logs/env_id/"
os.makedirs(log_dir, exist_ok=True)
# Create the callback: check every 1000 steps
callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
# env = Monitor(env, log_dir)
model = ACKTR(MlpPolicy, env, verbose=2)
model.load("RL_agent")
while True:
user_in = input("Enter States: ").split(',')
obs = [int(i) for i in user_in]
print(model.action_probability(obs))
action = model.predict(obs, deterministic = True)
print(action)