def init():
file = open('models/BreakoutNoFrameskip-v4.pkl', 'rb')
_, zoo_weights = pickle.load(file)
model = MyDQN(MyCnnPolicy,
Globals.env,
double_q=double_q,
learning_starts=learning_starts,
learning_rate=learning_rate,
tensorboard_log=tensorboard_log,
verbose=verbose,
exploration_fraction=exploration_fraction,
prioritized_replay=prioritized_replay,
exploration_final_eps=exploration_final_eps)
zoo_model = DQN(CnnPolicy,
Globals.env,
double_q=double_q,
learning_starts=learning_starts)
zoo_model.load_parameters(zoo_weights)
model.load_parameters(zoo_model.get_parameters(), exact_match=False)
params = model.get_parameters()
r = (np.random.rand(4, 4) - 0.5) * magnitude
params['deepq/model/action_value/fully_connected_1/biases:0'] = np.zeros(4)
params[
'deepq/model/action_value/fully_connected_1/weights:0'] = np.identity(
4) + r
model.load_parameters(params)
Globals.model = model
timeSteps=100000
if doTraining:
ppoModel.learn(total_timesteps=timeSteps)
dqnModel.learn(total_timesteps=timeSteps)
print("Training Finished")
if overWriteModels:
print("Overwriting Models")
ppoModel.save(ppoModelLocation)
dqnModel.save(dqnModelLocation)
with open('/home/john/ai-safety-gridworlds/logs/dqnparamsBefore.csv', 'w') as csvFile:
csvWriter = csv.writer(csvFile)
params = dqnModel.get_parameters()
csvWriter.writerow(params)
csvWriter.writerow(params.items())
with open('/home/john/ai-safety-gridworlds/logs/ppoparamsBefore.csv', 'w') as csvFile:
csvWriter = csv.writer(csvFile)
params = ppoModel.get_parameters()
csvWriter.writerow(params)
csvWriter.writerow(params.items())
#results_plotter.plot_results([log_dir], timeSteps, results_plotter.X_TIMESTEPS, "PPO Vase World")
#plt.show()
#meanReward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10, deterministic=False)
#print(meanReward, std_reward)
#print(evaluatePolicy(env, model, difficulties=[1,2,3,4,5]))
wallSize=[13,12,11,10,9,8,7,6,5,4,3,2,1]
