f = open(job_name_ewc_seed_lambda+'/trained_ewc_baseline.pickle', 'wb')
pickle.dump(baseline_ewc, f)
f.close()
f = open(job_name_ewc_seed_lambda+'/trained_ewc_alphas.pickle', 'wb')
pickle.dump(agent_ewc.theta, f)
f.close()
f = open(job_name_ewc_seed_lambda+'/trained_ewc_grads.pickle', 'wb')
pickle.dump(agent_ewc.grad, f)
f.close()
f = open(job_name_ewc_seed_lambda+'/trained_ewc_hess.pickle', 'wb')
pickle.dump(agent_ewc.hess, f)
f.close()
f = open(job_name_ewc_seed_lambda+'/env_factors.pickle', 'wb')
pickle.dump(size_factors_list, f)
f.close()
make_multitask_train_plots(loggers=agent_ewc.logger, keys=['stoc_pol_mean'], save_loc=job_name_ewc_seed_lambda+'/logs/')
mean_test_perf = agent_ewc.test_tasks(test_rollouts=10,
num_cpu=num_cpu)
result = np.mean(list(mean_test_perf.values()))
print(result)
make_multitask_test_plots(mean_test_perf, save_loc=job_name_ewc_seed_lambda+'/')
result_file = open(job_name_ewc_seed_lambda + '/results.txt', 'w')
result_file.write(str(mean_test_perf))
result_file.close()
SEED += 10
f = open(job_name_lpgftw_seed + '/trained_mtl_baseline.pickle', 'wb')
pickle.dump(baseline_mtl, f)
f.close()
f = open(job_name_lpgftw_seed + '/trained_mtl_alphas.pickle', 'wb')
pickle.dump(agent_mtl.theta, f)
f.close()
f = open(job_name_lpgftw_seed + '/trained_mtl_grads.pickle', 'wb')
pickle.dump(agent_mtl.grad, f)
f.close()
f = open(job_name_lpgftw_seed + '/trained_mtl_hess.pickle', 'wb')
pickle.dump(agent_mtl.hess, f)
f.close()
f = open(job_name_lpgftw_seed + '/task_order.pickle', 'wb')
pickle.dump(task_order, f)
f.close()
make_multitask_train_plots(loggers=agent_mtl.logger,
keys=['stoc_pol_mean'],
save_loc=job_name_lpgftw_seed + '/logs/')
mean_test_perf = agent_mtl.test_tasks(test_rollouts=10, num_cpu=num_cpu)
result = np.mean(list(mean_test_perf.values()))
print(result)
make_multitask_test_plots(mean_test_perf,
save_loc=job_name_lpgftw_seed + '/')
result_file = open(job_name_lpgftw_seed + '/results.txt', 'w')
result_file.write(str(mean_test_perf))
result_file.close()
SEED += 10
evaluation_rollouts=5,
task_id=task_id)
agent_mtl.add_approximate_cost(N=10, task_id=task_id, num_cpu=1)
train_agent(job_name=job_name_stl,
agent=agent_stl[task_id],
seed=SEED,
niter=50,
gamma=0.995,
gae_lambda=0.97,
num_cpu=num_cpu,
sample_mode='trajectories',
num_traj=10,
save_freq=5,
evaluation_rollouts=5,
task_id=task_id)
loggers_stl[task_id] = agent_stl[task_id].logger
print("time taken for linear policy training = %f" % (timer.time() - ts))
make_multitask_train_plots(loggers=agent_mtl.logger,
keys=['stoc_pol_mean'],
save_loc=job_name_mtl + '/logs/')
make_multitask_train_plots(loggers=loggers_stl,
keys=['stoc_pol_mean'],
save_loc=job_name_stl + '/logs/')
mean_test_perf = agent_mtl.test_tasks(test_rollouts=10, num_cpu=num_cpu)
result = np.mean(list(mean_test_perf.values()))
print(result)
make_multitask_test_plots(mean_test_perf, save_loc=job_name_mtl + '/')
