def create_act_model(sess, env, nenvs): ob_space = env.observation_space ac_space = env.action_space policy = policies.get_policy() act = policy(sess, ob_space, ac_space, nenvs, 1, reuse=False) return act
def main(): args = setup_utils.setup_and_load(num_levels=250, starting_level=0, paint_vel_info=1, run_id='start0numlev250_256mts_dann_low', num_envs=32) comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) utils.setup_mpi_gpus() #config = tf.ConfigProto() frac_gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3) frac_gpu_config = tf.ConfigProto(gpu_options=frac_gpu_options) nogpu_config = tf.ConfigProto(device_count={'GPU': 0}) #config.gpu_options.allow_growth = True # pylint: disable=E1101 nenvs = Config.NUM_ENVS print("Num envs: " + str(Config.NUM_ENVS)) total_timesteps = int(256e6) save_interval = args.save_interval env = utils.make_general_env(nenvs, seed=rank) with tf.Session(config=frac_gpu_config): #with tf.Session(config=nogpu_config): env = wrappers.add_final_wrappers(env) policy = policies.get_policy() ppo2.learn(policy=policy, env=env, save_interval=save_interval, nsteps=Config.NUM_STEPS, nminibatches=Config.NUM_MINIBATCHES, lam=0.95, gamma=Config.GAMMA, noptepochs=Config.PPO_EPOCHS, log_interval=1, ent_coef=Config.ENTROPY_COEFF, lr=lambda f: f * Config.LEARNING_RATE, cliprange=lambda f: f * 0.2, total_timesteps=total_timesteps)
def create_act_model(sess, env, nenvs): load_data = Config.get_load_data('default') create_additional = 'use_minimum_model' not in load_data['args'] ob_space = env.observation_space ac_space = env.action_space policy = policies.get_policy() act = policy(sess, ob_space, ac_space, nenvs, 1, reuse=False, create_additional=create_additional) return act
def main(): args = setup_utils.setup_and_load() comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) utils.setup_mpi_gpus() config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 nenvs = Config.NUM_ENVS total_timesteps = int(160e6) if Config.LONG_TRAINING: total_timesteps = int(200e6) elif Config.SHORT_TRAINING: total_timesteps = int(120e6) save_interval = args.save_interval env = utils.make_general_env(nenvs, seed=rank) with tf.compat.v1.Session(config=config): env = wrappers.add_final_wrappers(env) policy = policies.get_policy() ppo2.learn(policy=policy, env=env, save_interval=save_interval, nsteps=Config.NUM_STEPS, nminibatches=Config.NUM_MINIBATCHES, lam=0.95, gamma=Config.GAMMA, noptepochs=Config.PPO_EPOCHS, log_interval=1, ent_coef=Config.ENTROPY_COEFF, lr=lambda f : f * Config.LEARNING_RATE, cliprange=lambda f : f * 0.2, total_timesteps=total_timesteps)
def main(): print('Parsing args') args = setup_utils.setup_and_load() print('Setting up MPI') comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) print('Setting config') # coinrun version, allows you to specify how many GPUs you want this run to use #utils.setup_mpi_gpus() # baselines version, just sets the number of GPUs to the -n flag #setup_mpi_gpus() os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(Config.NUM_GPUS) config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 total_timesteps = int(1e6) save_interval = args.save_interval #env = utils.make_general_env(nenvs, seed=rank) #print (env) mpi_print(Config.ENVIRONMENT) # import ipdb;ipdb.set_trace() observation_space = Box(shape=(64,64,3),low=0,high=255) action_space = DiscreteG(5) venv = make_env(total_timesteps,observation_space, action_space) venv_eval = make_env(total_timesteps,observation_space, action_space) with tf.compat.v1.Session(config=config) as sess: #sess.run(tf.compat.v1.global_variables_initializer()) if Config.AGENT == 'ppo': from coinrun import ppo2 as agent from coinrun import policies elif Config.AGENT == 'ppo_rnd': from coinrun import ppo2_rnd as agent from coinrun import policies elif Config.AGENT == 'ppo_diayn': from coinrun import ppo2_diayn as agent from coinrun import policies elif Config.AGENT == 'ppg': from coinrun import ppo2_ppg as agent from coinrun import policies elif Config.AGENT == 'ppg_ssl': from coinrun import ppo2_ppg_ssl as agent from coinrun import policies elif Config.AGENT == 'ppo_goal': from coinrun import ppo2_goal as agent from coinrun import policies elif Config.AGENT == 'ppo_curl': from coinrun import ppo2_curl as agent from coinrun import policies elif Config.AGENT == 'ppo_goal_bogdan': from coinrun import ppo2_goal_bogdan as agent from coinrun import policies_bogdan as policies elif Config.AGENT == 'ppg_cluster': from coinrun import ppo2_ppg_sinkhorn as agent from coinrun import policies_ppg_sinkhorn as policies policy = policies.get_policy() agent.learn(policy=policy, env=venv, eval_env=venv_eval, save_interval=save_interval, nsteps=Config.NUM_STEPS, nminibatches=Config.NUM_MINIBATCHES, lam=0.95, gamma=Config.GAMMA, noptepochs=Config.PPO_EPOCHS, log_interval=1, ent_coef=Config.ENTROPY_COEFF, lr=lambda f : f * Config.LEARNING_RATE, cliprange=lambda f : f * 0.2, total_timesteps=total_timesteps)
def learn(*, policy, env, eval_env, nsteps, total_timesteps, ent_coef, lr, vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95, log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2, save_interval=0, load_path=None): comm = MPI.COMM_WORLD rank = comm.Get_rank() mpi_size = comm.Get_size() #tf.compat.v1.disable_v2_behavior() sess = tf.compat.v1.get_default_session() if isinstance(lr, float): lr = constfn(lr) else: assert callable(lr) if isinstance(cliprange, float): cliprange = constfn(cliprange) else: assert callable(cliprange) total_timesteps = int(total_timesteps) nenvs = env.num_envs ob_space = env.observation_space ac_space = env.action_space nbatch = nenvs * nsteps nbatch_train = nbatch // nminibatches model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, max_grad_norm=max_grad_norm) utils.load_all_params(sess) runner = Runner(env=env, eval_env=eval_env, model=model, nsteps=nsteps, gamma=gamma, lam=lam) epinfobuf10 = deque(maxlen=10) epinfobuf100 = deque(maxlen=100) eval_epinfobuf100 = deque(maxlen=100) tfirststart = time.time() active_ep_buf = epinfobuf100 eval_active_ep_buf = eval_epinfobuf100 nupdates = total_timesteps//nbatch mean_rewards = [] datapoints = [] run_t_total = 0 train_t_total = 0 can_save = False checkpoints = [32, 64] saved_key_checkpoints = [False] * len(checkpoints) if Config.SYNC_FROM_ROOT and rank != 0: can_save = False def save_model(base_name=None): base_dict = {'datapoints': datapoints} utils.save_params_in_scopes(sess, ['model'], Config.get_save_file(base_name=base_name), base_dict) # For logging purposes, allow restoring of update start_update = 0 if Config.RESTORE_STEP is not None: start_update = Config.RESTORE_STEP // nbatch z_iter = 0 curr_z = np.random.randint(0, high=Config.POLICY_NHEADS) tb_writer = TB_Writer(sess) import os os.environ["WANDB_API_KEY"] = "02e3820b69de1b1fcc645edcfc3dd5c5079839a1" os.environ["WANDB_SILENT"] = "true" run_id = np.random.randint(100000000) os.environ["WANDB_RUN_ID"] = str(run_id) group_name = "%s__%s__%f__%f" %(Config.ENVIRONMENT,Config.RUN_ID,Config.REP_LOSS_WEIGHT, Config.TEMP) name = "%s__%s__%f__%f__%d" %(Config.ENVIRONMENT,Config.RUN_ID,Config.REP_LOSS_WEIGHT, Config.TEMP, run_id) wandb.init(project='ising_generalization' if Config.ENVIRONMENT == 'ising' else 'procgen_generalization' , entity='ssl_rl', config=Config.args_dict, group=group_name, name=name, mode="disabled" if Config.DISABLE_WANDB else "online") api = wandb.Api() list_runs = api.runs("ssl_rl/procgen_generalization") single_level_runs=[run for run in list_runs if 'ppo_per_level' in run.name] non_crashed = [run for run in single_level_runs if run.state in ['running','finished']] game_runs = [run for run in non_crashed if Config.ENVIRONMENT in run.name] wandb_save_dir = '%s/%s'%(Config.RESTORE_PATH,Config.ENVIRONMENT) print('Save dir: %s'%wandb_save_dir) if not os.path.isdir(wandb_save_dir): import requests for run in game_runs: level_id = run.name.split('__')[-1] run_save_dir = wandb_save_dir + '/' + level_id if not os.path.isdir(run_save_dir): os.makedirs(run_save_dir) def save_wandb_file(name): url = "https://api.wandb.ai/files/ssl_rl/procgen_generalization/%s/%s"%(run.id,name) r = requests.get(url) with open(run_save_dir+'/%s'%name , 'wb') as fh: fh.write(r.content) save_wandb_file('checkpoint') save_wandb_file('ppo-1.data-00000-of-00001') save_wandb_file('ppo-1.index') save_wandb_file('ppo-1.meta') print('Downloaded level id %s to %s (run id: %s)' % (level_id,run_save_dir,run.id) ) print(os.listdir(run_save_dir)) # wandb.restore(wandb_save_dir+"/checkpoint",run_path='/'.join(run.path)) # load in just the graph and model parameters outside for-loop from coinrun import policies as policies_ppo ppo = policies_ppo.get_policy() ppo_graph_1, ppo_graph_2 = tf.Graph(), tf.Graph() PSE_policy = Config.PSE_POLICY if PSE_policy == 'ppo_2': levels = np.unique(os.listdir(wandb_save_dir)).astype(int) if Config.ENVIRONMENT == 'bigfish': levels = np.setdiff1d(levels,np.array([4])) pse_replay = [] for mdp_id in levels: print('Collecting MDP %d'%mdp_id) mb_obs_i, mb_actions_i, mb_rewards_i = generate_level_replay(ppo,mdp_id,wandb_save_dir,nbatch_train, nsteps, max_grad_norm, ob_space, ac_space, nsteps_rollout=782) pse_replay.append([mb_obs_i, mb_actions_i, mb_rewards_i]) for update in range(start_update+1, nupdates+1): assert nbatch % nminibatches == 0 nbatch_train = nbatch // nminibatches tstart = time.time() frac = 1.0 - (update - 1.0) / nupdates lrnow = lr(frac) cliprangenow = cliprange(frac) # mpi_print('collecting rollouts...') run_tstart = time.time() packed = runner.run(update_frac=update/nupdates) obs, returns, masks, actions, values, neglogpacs, infos, rewards, epinfos, eval_epinfos = packed values_i = returns_i = states_nce = anchors_nce = labels_nce = actions_nce = neglogps_nce = rewards_nce = infos_nce = None """ PSE data re-collection 1. Make 2 envs for respective policies for 2 random levels """ levels = np.unique(os.listdir(wandb_save_dir)).astype(int) if Config.ENVIRONMENT == 'bigfish': levels = np.setdiff1d(levels,np.array([4])) mdp_1,mdp_2 = np.random.choice(levels,size=2,replace=False) # import ipdb;ipdb.set_trace() observation_space = Dict(rgb=Box(shape=(64,64,3),low=0,high=255)) action_space = DiscreteG(15) gym3_env_eval_1 = ProcgenGym3Env(num=Config.NUM_ENVS, env_name=Config.ENVIRONMENT, num_levels=1, start_level=int(mdp_1), paint_vel_info=Config.PAINT_VEL_INFO, distribution_mode=Config.FIRST_PHASE) venv_eval_1 = FakeEnv(gym3_env_eval_1, observation_space, action_space) venv_eval_1 = VecExtractDictObs(venv_eval_1, "rgb") venv_eval_1 = VecMonitor( venv=venv_eval_1, filename=None, keep_buf=100, ) venv_eval_1 = VecNormalize(venv=venv_eval_1, ob=False) venv_eval_1 = wrappers.add_final_wrappers(venv_eval_1) gym3_env_eval_2 = ProcgenGym3Env(num=Config.NUM_ENVS, env_name=Config.ENVIRONMENT, num_levels=1, start_level=int(mdp_2), paint_vel_info=Config.PAINT_VEL_INFO, distribution_mode=Config.FIRST_PHASE) venv_eval_2 = FakeEnv(gym3_env_eval_2, observation_space, action_space) venv_eval_2 = VecExtractDictObs(venv_eval_2, "rgb") venv_eval_2 = VecMonitor( venv=venv_eval_2, filename=None, keep_buf=100, ) venv_eval_2 = VecNormalize(venv=venv_eval_2, ob=False) venv_eval_2 = wrappers.add_final_wrappers(venv_eval_2) def random_policy(states): actions = np.random.randint(0,15,Config.NUM_ENVS) return actions # print('Loading weights from %s'%(wandb_save_dir+'/%d/ppo-1'%mdp_1)) # with ppo_graph.as_default(): # ppo_model = ppo(sess, ob_space, ac_space, nbatch_train, nsteps, max_grad_norm, override_agent='ppo') #import ipdb;ipdb.set_trace() # NOTE: this is recreating a graph within the updates, I'm moving them outside the training loop if PSE_policy == 'ppo': print('Using pretrained PPO policy') model1_path = wandb_save_dir+'/%d/ppo-1'%mdp_1 model2_path = wandb_save_dir+'/%d/ppo-1'%mdp_2 graph_one_vars = ppo_graph_1.get_all_collection_keys() with tf.compat.v1.Session(graph=ppo_graph_1,config=tf.ConfigProto(inter_op_parallelism_threads=1,intra_op_parallelism_threads=1)) as sess_1: with tf.compat.v1.variable_scope("model_1"): ppo_model_1 = ppo(sess_1, ob_space, ac_space, nbatch_train, nsteps, max_grad_norm, override_agent='ppo') initialize = tf.compat.v1.global_variables_initializer() sess_1.run(initialize) model_saver = tf.train.import_meta_graph(model1_path+'.meta') model_saver.restore(sess_1, save_path=model1_path) mb_obs_1, mb_actions_1, mb_rewards_1 = collect_data(ppo_model_1,venv_eval_1,nsteps=32, param_vals='pretrained') with tf.compat.v1.Session(graph=ppo_graph_2,config=tf.ConfigProto(inter_op_parallelism_threads=1,intra_op_parallelism_threads=1)) as sess_2: with tf.compat.v1.variable_scope("model_2"): ppo_model_2 = ppo(sess_2, ob_space, ac_space, nbatch_train, nsteps, max_grad_norm, override_agent='ppo') initialize = tf.compat.v1.global_variables_initializer() sess_2.run(initialize) model_saver = tf.train.import_meta_graph(model2_path+'.meta') model_saver.restore(sess_2, save_path=model2_path) mb_obs_2, mb_actions_2, mb_rewards_2 = collect_data(ppo_model_2,venv_eval_2,nsteps=32, param_vals='pretrained') elif PSE_policy == 'random': print('Using random uniform policy') mb_obs_1, mb_actions_1, mb_rewards_1 = collect_data(random_policy,venv_eval_1,nsteps=32, param_vals='random') mb_obs_2, mb_actions_2, mb_rewards_2 = collect_data(random_policy,venv_eval_2,nsteps=32, param_vals='random') elif PSE_policy == 'ppo_2': mdp_1,mdp_2 = np.random.choice(np.arange(len(pse_replay)),size=2,replace=False) mb_obs_1, mb_actions_1, mb_rewards_1 = pse_replay[mdp_1] mb_obs_2, mb_actions_2, mb_rewards_2 = pse_replay[mdp_2] # reshape our augmented state vectors to match first dim of observation array # (mb_size*num_envs, 64*64*RGB) # (mb_size*num_envs, num_actions) avg_value = np.mean(values) epinfobuf10.extend(epinfos) epinfobuf100.extend(epinfos) eval_epinfobuf100.extend(eval_epinfos) run_elapsed = time.time() - run_tstart run_t_total += run_elapsed # mpi_print('rollouts complete') mblossvals = [] # mpi_print('updating parameters...') train_tstart = time.time() mean_cust_loss = 0 inds = np.arange(nbatch) inds_pse = np.arange(1024) inds_nce = np.arange(nbatch//runner.nce_update_freq) for _ in range(noptepochs): np.random.shuffle(inds) np.random.shuffle(inds_nce) for start in range(0, nbatch, nbatch_train): sess.run([model.train_model.train_dropout_assign_ops]) end = start + nbatch_train mbinds = inds[start:end] slices = (arr[mbinds] for arr in (obs, returns, masks, actions, infos, values, neglogpacs, rewards)) slices_pse_1 = (arr[inds_pse] for arr in (mb_obs_1, mb_actions_1, mb_rewards_1)) slices_pse_2 = (arr[inds_pse] for arr in (mb_obs_2, mb_actions_2, mb_rewards_2)) mblossvals.append(model.train(lrnow, cliprangenow, *slices, *slices_pse_1, *slices_pse_2, train_target='policy')) slices = (arr[mbinds] for arr in (obs, returns, masks, actions, infos, values, neglogpacs, rewards)) np.random.shuffle(inds_pse) slices_pse_1 = (arr[inds_pse] for arr in (mb_obs_1, mb_actions_1, mb_rewards_1)) slices_pse_2 = (arr[inds_pse] for arr in (mb_obs_2, mb_actions_2, mb_rewards_2)) model.train(lrnow, cliprangenow, *slices, *slices_pse_1, *slices_pse_2, train_target='pse') # update the dropout mask sess.run([model.train_model.train_dropout_assign_ops]) sess.run([model.train_model.run_dropout_assign_ops]) train_elapsed = time.time() - train_tstart train_t_total += train_elapsed # mpi_print('update complete') lossvals = np.mean(mblossvals, axis=0) tnow = time.time() fps = int(nbatch / (tnow - tstart)) if update % log_interval == 0 or update == 1: step = update*nbatch eval_rew_mean = utils.process_ep_buf(eval_active_ep_buf, tb_writer=tb_writer, suffix='_eval', step=step) rew_mean_10 = utils.process_ep_buf(active_ep_buf, tb_writer=tb_writer, suffix='', step=step) ep_len_mean = np.nanmean([epinfo['l'] for epinfo in active_ep_buf]) mpi_print('\n----', update) mean_rewards.append(rew_mean_10) datapoints.append([step, rew_mean_10]) tb_writer.log_scalar(ep_len_mean, 'ep_len_mean', step=step) tb_writer.log_scalar(fps, 'fps', step=step) tb_writer.log_scalar(avg_value, 'avg_value', step=step) tb_writer.log_scalar(mean_cust_loss, 'custom_loss', step=step) mpi_print('time_elapsed', tnow - tfirststart, run_t_total, train_t_total) mpi_print('timesteps', update*nsteps, total_timesteps) # eval_rew_mean = episode_rollouts(eval_env,model,step,tb_writer) mpi_print('eplenmean', ep_len_mean) mpi_print('eprew', rew_mean_10) mpi_print('eprew_eval', eval_rew_mean) mpi_print('fps', fps) mpi_print('total_timesteps', update*nbatch) mpi_print([epinfo['r'] for epinfo in epinfobuf10]) rep_loss = 0 if len(mblossvals): for (lossval, lossname) in zip(lossvals, model.loss_names): mpi_print(lossname, lossval) tb_writer.log_scalar(lossval, lossname, step=step) mpi_print('----\n') wandb.log({"%s/eprew"%(Config.ENVIRONMENT):rew_mean_10, "%s/eprew_eval"%(Config.ENVIRONMENT):eval_rew_mean, "%s/custom_step"%(Config.ENVIRONMENT):step}) if can_save: if save_interval and (update % save_interval == 0): save_model() for j, checkpoint in enumerate(checkpoints): if (not saved_key_checkpoints[j]) and (step >= (checkpoint * 1e6)): saved_key_checkpoints[j] = True save_model(str(checkpoint) + 'M') save_model() env.close() # import subprocess # wandb_files = os.listdir('wandb') # file_to_save = '' # for fn in wandb_files: # if str(run_id) in fn: # file_to_save = fn # break # print(file_to_save) # my_env = os.environ.copy() # my_env["WANDB_API_KEY"] = "02e3820b69de1b1fcc645edcfc3dd5c5079839a1" # subprocess.call(['wandb','sync','wandb/'+ file_to_save],env=my_env) return mean_rewards
def main(): args = setup_utils.setup_and_load() comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) # coinrun version, allows you to specify how many GPUs you want this run to use #utils.setup_mpi_gpus() # baselines version, just sets the number of GPUs to the -n flag #setup_mpi_gpus() os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(Config.NUM_GPUS) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 nenvs = Config.NUM_ENVS total_timesteps = int(160e6) if Config.LONG_TRAINING: total_timesteps = int(200e6) elif Config.SHORT_TRAINING: #total_timesteps = int(120e6) total_timesteps = int(25e6) elif Config.VERY_SHORT_TRAINING: total_timesteps = int(5e6) save_interval = args.save_interval #env = utils.make_general_env(nenvs, seed=rank) #print (env) print(Config.ENVIRONMENT) baseline_vec = ProcgenEnv(num_envs=nenvs, env_name=Config.ENVIRONMENT, num_levels=Config.NUM_LEVELS, paint_vel_info=Config.PAINT_VEL_INFO, distribution_mode="easy") gym3_env = ProcgenGym3Env(num=nenvs, env_name=Config.ENVIRONMENT, num_levels=Config.NUM_LEVELS, paint_vel_info=Config.PAINT_VEL_INFO, distribution_mode="easy") venv = FakeEnv(gym3_env, baseline_vec) venv = VecExtractDictObs(venv, "rgb") venv = VecMonitor( venv=venv, filename=None, keep_buf=100, ) venv = VecNormalize(venv=venv, ob=False) #sys.exit(0) with tf.Session(config=config) as sess: #env = wrappers.add_final_wrappers(env) venv = wrappers.add_final_wrappers(venv) policy = policies.get_policy() #sess.run(tf.global_variables_initializer()) ppo2.learn( policy=policy, env=venv, #env=env, save_interval=save_interval, nsteps=Config.NUM_STEPS, nminibatches=Config.NUM_MINIBATCHES, lam=0.95, gamma=Config.GAMMA, noptepochs=Config.PPO_EPOCHS, log_interval=1, ent_coef=Config.ENTROPY_COEFF, lr=lambda f: f * Config.LEARNING_RATE, cliprange=lambda f: f * 0.2, total_timesteps=total_timesteps)
def main(): print('Parsing args') args = setup_utils.setup_and_load() print('Setting up MPI') comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) print('Setting config') # coinrun version, allows you to specify how many GPUs you want this run to use #utils.setup_mpi_gpus() # baselines version, just sets the number of GPUs to the -n flag #setup_mpi_gpus() os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(Config.NUM_GPUS) config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 total_timesteps = int(160e6) if Config.LONG_TRAINING: total_timesteps = int(25e6) elif Config.SHORT_TRAINING: total_timesteps = int(8e6) elif Config.VERY_SHORT_TRAINING: total_timesteps = int(500e3) elif Config.VERY_VERY_SHORT_TRAINING: total_timesteps = int(50e3) save_interval = args.save_interval #env = utils.make_general_env(nenvs, seed=rank) #print (env) mpi_print(Config.ENVIRONMENT) venv, venv_train, venv_adapt = make_env(total_timesteps//2) #switch "easy" -> "exploration" halfway # import ipdb;ipdb.set_trace() observation_space = Dict(rgb=Box(shape=(64,64,3),low=0,high=255)) action_space = DiscreteG(15) # baseline_vec_eval = ProcgenEnv(num_envs=Config.NUM_ENVS, env_name=Config.ENVIRONMENT, num_levels=0, paint_vel_info=Config.PAINT_VEL_INFO, distribution_mode=Config.FIRST_PHASE) gym3_env_eval = ProcgenGym3Env(num=Config.NUM_ENVS, env_name=Config.ENVIRONMENT, num_levels=0, paint_vel_info=Config.PAINT_VEL_INFO, distribution_mode=Config.FIRST_PHASE) venv_eval = FakeEnv(gym3_env_eval, observation_space, action_space) venv_eval = VecExtractDictObs(venv_eval, "rgb") venv_eval = VecMonitor( venv=venv_eval, filename=None, keep_buf=100, ) venv_eval = VecNormalize(venv=venv_eval, ob=False) venv_eval = wrappers.add_final_wrappers(venv_eval) with tf.compat.v1.Session(config=config) as sess: if Config.AGENT == 'ppo': from coinrun import ppo2 as agent from coinrun import policies elif Config.AGENT == 'ppo_rnd': from coinrun import ppo2_rnd as agent from coinrun import policies elif Config.AGENT == 'ppo_diayn': from coinrun import ppo2_diayn as agent from coinrun import policies elif Config.AGENT == 'ppg': from coinrun import ppo2_ppg as agent from coinrun import policies elif Config.AGENT == 'ppg_ssl': from coinrun import ppo2_ppg_ssl as agent from coinrun import policies elif Config.AGENT == 'ppo_goal': from coinrun import ppo2_goal as agent from coinrun import policies elif Config.AGENT == 'ppo_curl': from coinrun import ppo2_curl as agent from coinrun import policies elif Config.AGENT == 'ppo_goal_bogdan' or Config.AGENT == 'ppo_ctrl': from coinrun import ppo2_goal_bogdan as agent from coinrun import policies_bogdan as policies elif Config.AGENT == 'ppg_cluster': from coinrun import ppo2_ppg_sinkhorn as agent from coinrun import policies_ppg_sinkhorn as policies elif Config.AGENT == 'ppo_bisimulation': from coinrun import ppo2_bisimulation as agent from coinrun import policies_bisimulation as policies elif Config.AGENT == 'ppo_pse': from coinrun import ppo2_pse as agent from coinrun import policies_pse as policies policy = policies.get_policy() final_eprew_eval = agent.learn(policy=policy, env=venv, eval_env=venv_eval, save_interval=save_interval, nsteps=Config.NUM_STEPS, nminibatches=Config.NUM_MINIBATCHES, lam=0.95, gamma=Config.GAMMA, noptepochs=Config.PPO_EPOCHS, log_interval=1, #10, ent_coef=Config.ENTROPY_COEFF, lr=lambda f : f * Config.LEARNING_RATE, lr_ctrl=lambda f : f * Config.LEARNING_RATE_CTRL, lr_myow=lambda f : f * Config.LEARNING_RATE_MYOW, cliprange=lambda f : f * 0.2, total_timesteps=total_timesteps) return final_eprew_eval
def main(): # general setup os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' args = setup_utils.setup_and_load() comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) utils.setup_mpi_gpus() config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 # perpare directory sub_dir = utils.file_to_path(Config.get_save_file(base_name="tmp")) if os.path.isdir(sub_dir): shutil.rmtree(path=sub_dir) os.mkdir(sub_dir) # hyperparams nenvs = Config.NUM_ENVS total_timesteps = Config.TIMESTEPS population_size = Config.POPULATION_SIZE timesteps_per_agent = Config.TIMESTEPS_AGENT worker_count = Config.WORKER_COUNT passthrough_perc = Config.PASSTHROUGH_PERC mutating_perc = Config.MUTATING_PERC # create environment def make_env(): env = utils.make_general_env(nenvs, seed=rank) env = wrappers.add_final_wrappers(env) return env # setup session and workers, and therefore tensorflow ops graph = tf.get_default_graph() sess = tf.Session(graph=graph) policy = policies.get_policy() workers = [ Worker(sess, i, nenvs, make_env, policy, sub_dir) for i in range(worker_count) ] tb_writer = TB_Writer(sess) def clean_exit(): for worker in workers: Thread.join(worker.thread) utils.mpi_print("") utils.mpi_print("== total duration", "{:.1f}".format(time.time() - t_first_start), " s ==") utils.mpi_print(" exit...") # save best performing agent population.sort(key=lambda k: k['fit'], reverse=True) workers[0].restore_model(name=population[0]["name"]) workers[0].dump_model() # cleanup sess.close() shutil.rmtree(path=sub_dir) # load data from restore point and seed the whole population loaded_name = None if workers[0].try_load_model(): loaded_name = str(uuid.uuid1()) workers[0].save_model(name=loaded_name) # initialise population # either all random and no mutations pending # or all from restore point with all but one to be mutated population = [{ "name": loaded_name or str(uuid.uuid1()), "fit": -1, "need_mut": loaded_name != None and i != 0, "age": -1, "mean_ep_len": -1 } for i in range(population_size)] utils.mpi_print("== population size", population_size, ", t_agent ", timesteps_per_agent, " ==") t_first_start = time.time() try: # main loop generation = 0 timesteps_done = 0 while timesteps_done < total_timesteps: t_generation_start = time.time() utils.mpi_print("") utils.mpi_print("__ Generation", generation, " __") # initialise and evaluate all new agents for agent in population: #if agent["fit"] < 0: # test/ if True: # test constant reevaluation, to dismiss "lucky runs" -> seems good # pick worker from pool and let it work on the agent not_in_work = True while not_in_work: for worker in workers: if worker.can_take_work(): worker.work(agent, timesteps_per_agent) not_in_work = False break timesteps_done += timesteps_per_agent * nenvs for worker in workers: Thread.join(worker.thread) # sort by fitness population.sort(key=lambda k: k["fit"], reverse=True) # print stuff fitnesses = [agent["fit"] for agent in population] ages = [agent["age"] for agent in population] ep_lens = [agent["mean_ep_len"] for agent in population] utils.mpi_print(*["{:5.3f}".format(f) for f in fitnesses]) utils.mpi_print(*["{:5}".format(a) for a in ages]) utils.mpi_print("__ average fit", "{:.1f}".format( np.mean(fitnesses)), ", t_done", timesteps_done, ", took", "{:.1f}".format(time.time() - t_generation_start), "s", ", total", "{:.1f}".format(time.time() - t_first_start), "s __") # log stuff tb_writer.log_scalar(np.mean(fitnesses), "mean_fit", timesteps_done) tb_writer.log_scalar(np.median(fitnesses), "median_fit", timesteps_done) tb_writer.log_scalar(np.max(fitnesses), "max_fit", timesteps_done) tb_writer.log_scalar(np.mean(ages), "mean_age", timesteps_done) ep_lens_mean = np.nanmean(ep_lens) if (ep_lens_mean): tb_writer.log_scalar(ep_lens_mean, "mean_ep_lens", timesteps_done) # cleanup to prevent disk clutter to_be_removed = set( re.sub(r'\..*$', '', f) for f in os.listdir(sub_dir)) - set( [agent["name"] for agent in population]) for filename in to_be_removed: os.remove(sub_dir + "/" + filename + ".index") os.remove(sub_dir + "/" + filename + ".data-00000-of-00001") # break when times up if not timesteps_done < total_timesteps: break # mark weak agents for replacement cutoff_passthrough = math.floor(population_size * passthrough_perc) cutoff_mutating = math.floor(population_size * mutating_perc) source_agents = population[:cutoff_mutating] new_population = population[:cutoff_passthrough] k = 0 while len(new_population) < population_size: new_agent = { "name": source_agents[k] ["name"], # Take name from source agent, so mutation knows the parent "fit": -1, "need_mut": True, "age": 0 } new_population.append(new_agent) k = (k + 1) % len(source_agents) population = new_population generation += 1 clean_exit() except KeyboardInterrupt: clean_exit() return 0
def main(): print('Parsing args') args = setup_utils.setup_and_load() print('Setting up MPI') comm = MPI.COMM_WORLD rank = comm.Get_rank() seed = int(time.time()) % 10000 set_global_seeds(seed * 100 + rank) print('Setting config') os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(Config.NUM_GPUS) config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 if Config.LONG_TRAINING: total_timesteps = int(1e6) elif Config.SHORT_TRAINING: total_timesteps = int(100e3) elif Config.MEDIUM_TRAINING: total_timesteps = int(200e3) save_interval = args.save_interval mpi_print(Config.ENVIRONMENT) n_tasks_train = Config.NUM_LEVELS n_tasks_test = 30 env_train = MultiTaskWrapper(lambda: make_env(Config.ENVIRONMENT, seed), n_tasks_train, offset=0) env_test = MultiTaskWrapper(lambda: make_env(Config.ENVIRONMENT, seed), n_tasks_test, offset=n_tasks_train) with tf.compat.v1.Session(config=config) as sess: if Config.AGENT == 'ppo': from coinrun import ppo2 as agent from coinrun import policies elif Config.AGENT == 'ppo_rnd': from coinrun import ppo2_rnd as agent from coinrun import policies elif Config.AGENT == 'ppo_diayn': from coinrun import ppo2_diayn as agent from coinrun import policies elif Config.AGENT == 'ppg': from coinrun import ppo2_ppg as agent from coinrun import policies elif Config.AGENT == 'ppg_ssl': from coinrun import ppo2_ppg_ssl as agent from coinrun import policies elif Config.AGENT == 'ppo_goal': from coinrun import ppo2_goal as agent from coinrun import policies elif Config.AGENT == 'ppo_curl': from coinrun import ppo2_curl as agent from coinrun import policies elif Config.AGENT == 'ppo_goal_bogdan': from coinrun import ppo2_goal_bogdan as agent from coinrun import policies_bogdan as policies elif Config.AGENT == 'ppg_cluster': from coinrun import ppo2_ppg_sinkhorn as agent from coinrun import policies_ppg_sinkhorn as policies elif Config.AGENT == 'ppo_bisimulation': from coinrun import ppo2_bisimulation as agent from coinrun import policies_bisimulation as policies elif Config.AGENT == 'ppo_pse': from coinrun import ppo2_pse as agent from coinrun import policies_pse as policies elif Config.AGENT == 'ppo_ctrl_cts': from coinrun import ppo2_ctrl_cts as agent from coinrun import policies_ctrl_cts as policies policy = policies.get_policy() agent.learn(policy=policy, env=env_train, eval_env=env_test, save_interval=save_interval, nsteps=Config.NUM_STEPS, nminibatches=Config.NUM_MINIBATCHES, lam=Config.GAE_LAMBDA, gamma=Config.GAMMA, noptepochs=Config.PPO_EPOCHS, log_interval=5, ent_coef=Config.ENTROPY_COEFF, lr=lambda f: f * Config.LEARNING_RATE, vf_coef=Config.VF_COEFF, cliprange=lambda f: f * 0.2, total_timesteps=total_timesteps)