def run_agent(envs, parameters): '''Train an agent.''' alg = parameters['alg'] learning_rate = parameters['learning_rate'] gamma = parameters['gamma'] model_path = parameters['model_path'] set_global_seeds(parameters.get('seed')) dummy_env = OptVecEnv(envs) if alg == 'PPO': model = PPO2(MlpPolicy, dummy_env, gamma=gamma, learning_rate=learning_rate, verbose=1, nminibatches=dummy_env.num_envs) elif alg == 'A2C': model = A2C(MlpPolicy, dummy_env, gamma=gamma, learning_rate=learning_rate, verbose=1) else: model = DDPG(ddpg.MlpPolicy, dummy_env, gamma=gamma, verbose=1, actor_lr=learning_rate / 10, critic_lr=learning_rate) try: model.learn(total_timesteps=parameters.get('total_timesteps', 10**6)) except tf.errors.InvalidArgumentError: LOGGER.error('Possible Nan, %s', str((alg, learning_rate, gamma))) finally: dummy_env.close() model.save(str(model_path))
def run_test(config): """Stable baselines test Mandatory configuration settings: - 'continuous' agent - camera_settings enabled - stable_baselines enabled """ env = None try: # Create Environment env = make_env(config) env = DummyVecEnv([lambda: env]) # Initialize DDPG and start learning n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise( mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) model = DDPG(CnnPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise, random_exploration=0.8) model.learn(total_timesteps=10000) finally: if env: env.close() else: clear_carla(config.host, config.port) print("-----Carla Environment is closed-----")
def test_identity_ddpg(): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) """ env = DummyVecEnv([lambda: IdentityEnvBox(eps=0.5)]) std = 0.2 param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(std), desired_action_stddev=float(std)) model = DDPG("MlpPolicy", env, gamma=0.0, param_noise=param_noise, memory_limit=int(1e6)) model.learn(total_timesteps=20000, seed=0) n_trials = 1000 reward_sum = 0 set_global_seeds(0) obs = env.reset() for _ in range(n_trials): action, _ = model.predict(obs) obs, reward, _, _ = env.step(action) reward_sum += reward assert reward_sum > 0.9 * n_trials # Free memory del model, env
def explore(app, emulator, appium, timesteps, timer, save_policy, policy_dir, cycle, nb_train_steps=10, random_exploration=0.7): try: env = TimeFeatureWrapper(app) model = DDPG(MlpPolicy, env, verbose=1, random_exploration=random_exploration, nb_train_steps=nb_train_steps) callback = TimerCallback(timer=timer) model.learn(total_timesteps=timesteps, callback=callback) if save_policy: model.save(f'{policy_dir}{os.sep}{cycle}') return True except Exception: appium.restart_appium() if emulator is not None: emulator.restart_emulator() return False
def test_ddpg_normalization(): """ Test that observations and returns normalizations are properly saved and loaded. """ param_noise = AdaptiveParamNoiseSpec(initial_stddev=0.05, desired_action_stddev=0.05) model = DDPG('MlpPolicy', 'Pendulum-v0', memory_limit=50000, normalize_observations=True, normalize_returns=True, nb_rollout_steps=128, nb_train_steps=1, batch_size=64, param_noise=param_noise) model.learn(1000) obs_rms_params = model.sess.run(model.obs_rms_params) ret_rms_params = model.sess.run(model.ret_rms_params) model.save('./test_ddpg.zip') loaded_model = DDPG.load('./test_ddpg.zip') obs_rms_params_2 = loaded_model.sess.run(loaded_model.obs_rms_params) ret_rms_params_2 = loaded_model.sess.run(loaded_model.ret_rms_params) for param, param_loaded in zip(obs_rms_params + ret_rms_params, obs_rms_params_2 + ret_rms_params_2): assert np.allclose(param, param_loaded) del model, loaded_model if os.path.exists("./test_ddpg.zip"): os.remove("./test_ddpg.zip")
def train_policy_ddpg(env, policy, policy_args, total_timesteps, verbose=0, actor_lr=.5, critic_lr=.001): """ Parameters ---------- env : vectorized set of EncoderWrapper of a TimeLimit wrapper of a restartable env. policy : ddpg policy class policy_args : dict of keyword arguments for policy class total_timesteps : int, how many timesteps to train policy (i.e. 200000) """ # the noise objects for DDPG n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) model = DDPG(policy, env, verbose=verbose, param_noise=param_noise, action_noise=action_noise, policy_kwargs=policy_args, actor_lr=actor_lr, critic_lr=critic_lr) #model = PPO2(policy, env) model.learn(total_timesteps) return model
def main(env: PSMCartesianDDPGEnv): # the noise objects for DDPG n_actions = env.action.action_space.shape[0] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) model = DDPG(MlpPolicy, env, gamma=0.95, verbose=1, nb_train_steps=300, nb_rollout_steps=150, param_noise=param_noise, batch_size=128, action_noise=action_noise, random_exploration=0.05, normalize_observations=True, tensorboard_log="./ddpg_dvrk_tensorboard/", observation_range=(-1.5, 1.5), critic_l2_reg=0.01) model.learn(total_timesteps=4000000, log_interval=100, callback=CheckpointCallback( save_freq=100000, save_path="./ddpg_dvrk_tensorboard/")) model.save("./ddpg_robot_env")
def main(): # unpause Simulation so that robot receives data on all topics gazebo_connection.GazeboConnection().unpauseSim() # create node rospy.init_node('pickbot_gym', anonymous=True, log_level=rospy.FATAL) env = gym.make('Pickbot-v1') # the noise objects for DDPG n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) model = DDPG(MlpPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise) model.learn(total_timesteps=200000) print("Saving model to pickbot_model_ddpg_continuous_" + timestamp + ".pkl") model.save("pickbot_model_ddpg_continuous_" + timestamp)
def ddpg(env_id, timesteps, policy="MlpPolicy", log_interval=None, tensorboard_log=None, seed=None, load_weights=None): from stable_baselines import DDPG env = gym.make(env_id) n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) if load_weights is not None: model = DDPG.load(load_weights, env=env) else: model = DDPG(policy, env, verbose=1, param_noise=param_noise, action_noise=action_noise, tensorboard_log=tensorboard_log) callback = WandbRenderEnvCallback(model_name="ddpg", env_name=env_id) model.learn(total_timesteps=timesteps, log_interval=log_interval, callback=callback) save_model_weights(model, "ddpg", env_id, policy, seed=seed, path=".")
def main(): # create Environment env = iCubPushGymEnv(urdfRoot=robot_data.getDataPath(), renders=False, useIK=1, isDiscrete=0, rnd_obj_pose=0, maxSteps=2000, reward_type=0) # set seed seed = 1 tf.reset_default_graph() set_global_seed(seed) env.seed(seed) # set log monitor_dir = os.path.join(log_dir,'log') os.makedirs(monitor_dir, exist_ok=True) env = Monitor(env, monitor_dir+'/', allow_early_resets=True) # create agent model nb_actions = env.action_space.shape[-1] action_noise = NormalActionNoise(mean=np.zeros(nb_actions), sigma=float(0.5373) * np.ones(nb_actions)) model = DDPG('LnMlpPolicy', env, action_noise=action_noise, gamma=0.99, batch_size=16, normalize_observations=True,normalize_returns=False, memory_limit=100000, verbose=1, tensorboard_log=os.path.join(log_dir,'tb'),full_tensorboard_log=False) #start learning model.learn(total_timesteps=500000, seed=seed, callback=callback) # save model print("Saving model.pkl to ",log_dir) act.save(log_dir+"/final_model.pkl")
def train_DDPG(self, model_name, model_params=config.DDPG_PARAMS): """DDPG model""" from stable_baselines import DDPG from stable_baselines.ddpg.policies import DDPGPolicy from stable_baselines.common.noise import OrnsteinUhlenbeckActionNoise env_train = self.env n_actions = env_train.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) start = time.time() model = DDPG('MlpPolicy', env_train, batch_size=model_params['batch_size'], buffer_size=model_params['buffer_size'], param_noise=param_noise, action_noise=action_noise, verbose=model_params['verbose']) model.learn(total_timesteps=model_params['timesteps']) end = time.time() model.save(f"{config.TRAINED_MODEL_DIR}/{model_name}") print('Training time (DDPG): ', (end - start) / 60, ' minutes') return model
def train_DDPG(env_train, model_name, timesteps=50000): """DDPG model""" start = time.time() model = DDPG('MlpPolicy', env_train) model.learn(total_timesteps=timesteps) end = time.time() model.save(f"{config.TRAINED_MODEL_DIR}/{model_name}") print('Training time (DDPG): ', (end - start) / 60, ' minutes') return model
def DDPGAgent(multi_stock_env, num_episodes): models_folder = 'saved_models' rewards_folder = 'saved_rewards' env = DummyVecEnv([lambda: multi_stock_env]) # the noise objects for DDPG n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) # Hyper parameters GAMMA = 0.99 TAU = 0.001 BATCH_SIZE = 16 ACTOR_LEARNING_RATE = 0.0001 CRITIC_LEARNING_RATE = 0.001 BUFFER_SIZE = 500 print("\nRunning DDPG Agent...\n") model = DDPG(MlpPolicy, env, gamma = GAMMA, tau = TAU, batch_size = BATCH_SIZE, actor_lr = ACTOR_LEARNING_RATE, critic_lr = CRITIC_LEARNING_RATE, buffer_size = BUFFER_SIZE, verbose=1, param_noise=param_noise, action_noise=action_noise) model.learn(total_timesteps=50000) model.save(f'{models_folder}/rl/ddpg.h5') del model model = DDPG.load(f'{models_folder}/rl/ddpg.h5') obs = env.reset() portfolio_value = [] for e in range(num_episodes): action, _states = model.predict(obs) next_state, reward, done, info = env.step(action) print(f"episode: {e + 1}/{num_episodes}, episode end value: {info[0]['cur_val']:.2f}") portfolio_value.append(round(info[0]['cur_val'], 3)) # save portfolio value for each episode np.save(f'{rewards_folder}/rl/ddpg.npy', portfolio_value) print("\nDDPG Agent run complete and saved!") a = np.load(f'./saved_rewards/rl/ddpg.npy') print(f"\nCumulative Portfolio Value Average reward: {a.mean():.2f}, Min: {a.min():.2f}, Max: {a.max():.2f}") plt.plot(a) plt.title("Portfolio Value Per Episode (DDPG)") plt.ylabel("Portfolio Value") plt.xlabel("Episodes") plt.show()
def train_ddpg(): env = gimbal(5, 500) env = DummyVecEnv([lambda: env]) eval_env = gimbal(5, 500) eval_env = DummyVecEnv([lambda: eval_env]) # the noise objects for DDPG n_actions = env.action_space.shape[-1] param_noise = None action_noise = None model = DDPG(policy=MlpPolicy, env=env, gamma=0.99, memory_policy=None, eval_env=eval_env, nb_train_steps=500, nb_rollout_steps=500, nb_eval_steps=500, param_noise=param_noise, action_noise=action_noise, normalize_observations=False, tau=0.001, batch_size=128, param_noise_adaption_interval=50, normalize_returns=False, enable_popart=False, observation_range=(-5000.0, 5000.0), critic_l2_reg=0.0, return_range=(-inf, inf), actor_lr=0.0001, critic_lr=0.001, clip_norm=None, reward_scale=1.0, render=False, render_eval=False, memory_limit=50000, verbose=1, tensorboard_log="./logs", _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False) #model = DDPG.load("./models/baseline_ddpg_t2") #model.set_env(env) model.learn(total_timesteps=1000000, callback=None, seed=None, log_interval=100, tb_log_name='DDPG', reset_num_timesteps=True) model.save("./models/baseline_ddpg_t2")
def test_ddpg_eval_env(): """ Additional test to check that everything is working when passing an eval env. """ eval_env = gym.make("Pendulum-v0") model = DDPG("MlpPolicy", "Pendulum-v0", nb_rollout_steps=5, nb_train_steps=2, nb_eval_steps=10, eval_env=eval_env, verbose=0) model.learn(1000)
def run(self): self._init() env = self.env model = self.model objective = self.objective if objective == "infogain": wenv = InfogainEnv(env, model) elif objective == "prederr": wenv = PrederrEnv(env, model) else: raise AttributeError( "Objective '{}' is unknown. Needs to be 'infogain' or 'prederr'" .format(objective)) wenv.max_episode_len = self.horizon wenv.end_episode_callback = self._end_episode dvenv = DummyVecEnv([lambda: wenv]) if self.rl_algo == "ddpg": self.logger.info("Setting up DDPG as model-free RL algorithm.") pn = AdaptiveParamNoiseSpec() an = NormalActionNoise(np.array([0]), np.array([1])) rl_model = DDPG(DDPGMlpPolicy, dvenv, verbose=1, render=False, action_noise=an, param_noise=pn, nb_rollout_steps=self.horizon, nb_train_steps=self.horizon) elif self.rl_algo == "sac": self.logger.info("Setting up SAC as model-free RL algorithm.") rl_model = SAC(SACMlpPolicy, dvenv, verbose=1, learning_starts=self.horizon) else: raise AttributeError( "Model-free RL algorithm '{}' is unknown.".format( self.rl_algo)) # Train the agent max_steps_total = self.horizon * self.n_episodes * 100 try: self.logger.info("Start the agent") rl_model.learn(total_timesteps=max_steps_total, seed=self.seed) except MaxEpisodesReachedException: print("Exploration finished.")
def train_DDPG(env_train, model_name, timesteps=10000): """DDPG model""" # the noise objects for DDPG n_actions = env_train.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) start = time.time() model = DDPG('MlpPolicy', env_train, param_noise=param_noise, action_noise=action_noise) model.learn(total_timesteps=timesteps) end = time.time() model.save(f"{config.TRAINED_MODEL_DIR}/{model_name}") print('Training time (DDPG): ', (end - start) / 60, ' minutes') return model
def __call__(self, trial): # Calculate an objective value by using the extra arguments. env_id = 'gym_custom:fooCont-v0' env = gym.make(env_id, data=self.train_data) env = DummyVecEnv([lambda: env]) algo = trial.suggest_categorical('algo', ['TD3']) model = 0 if algo == 'PPO2': policy_choice = trial.suggest_categorical('policy', [False, True]) policy = commonMlp if policy_choice else commonMlpLstm model_params = optimize_ppo2(trial) model = PPO2(policy, env, verbose=0, nminibatches=1, **model_params) model.learn(276*7000) elif algo == 'DDPG': policy_choice = trial.suggest_categorical('policy', [False, True]) policy = ddpgLnMlp model_params = sample_ddpg_params(trial) model= DDPG(policy, env, verbose=0, **model_params) model.learn(276*7000) elif algo == 'TD3': policy_choice = trial.suggest_categorical('policy', [False, True]) policy = td3MLP if policy_choice else td3LnMlp model_params = sample_td3_params(trial) model = TD3(policy, env, verbose=0, **model_params) model.learn(276*7000*3) rewards = [] reward_sum = 0.0 env = gym.make(env_id, data=self.test_data) env = DummyVecEnv([lambda: env]) obs = env.reset() for ep in range(1000): for step in range(276): action, _ = model.predict(obs) obs, reward, done, _ = env.step(action) reward_sum += reward if done: rewards.append(reward_sum) reward_sum = 0.0 obs = env.reset()
def main(output_folder_path:Path): # Set gym-carla environment agent_config = AgentConfig.parse_file(Path("configurations/agent_configuration.json")) carla_config = CarlaConfig.parse_file(Path("configurations/carla_configuration.json")) params = { "agent_config": agent_config, "carla_config": carla_config, "ego_agent_class": RLPIDAgent, "max_collision": 5 } env = gym.make('roar-pid-v0', params=params) env.reset() model_params: dict = { "verbose": 1, "render": True, "tensorboard_log": (output_folder_path / "tensorboard").as_posix() } latest_model_path = find_latest_model(output_folder_path) if latest_model_path is None: model = DDPG(LnMlpPolicy, env=env, **model_params) # full tensorboard log can take up space quickly else: model = DDPG.load(latest_model_path, env=env, **model_params) model.render = True model.tensorboard_log = (output_folder_path / "tensorboard").as_posix() logging_callback = LoggingCallback(model=model) checkpoint_callback = CheckpointCallback(save_freq=1000, verbose=2, save_path=(output_folder_path / "checkpoints").as_posix()) event_callback = EveryNTimesteps(n_steps=100, callback=checkpoint_callback) callbacks = CallbackList([checkpoint_callback, event_callback, logging_callback]) model = model.learn(total_timesteps=int(1e10), callback=callbacks, reset_num_timesteps=False) model.save(f"pid_ddpg_{datetime.now()}")
def main(): env1 = KukaDiverseObjectEnv(renders=True, isDiscrete=False) model = DDPG(MlpPolicy, env1, verbose=1) # = deepq.models.mlp([64]) model.learn(total_timesteps=500000) #max_timesteps=10000000, # exploration_fraction=0.1, # exploration_final_eps=0.02, # print_freq=10, # callback=callback, network='mlp') print("Saving model to kukadiverse_model.pkl") model.save("kukadiversecont_model.pkl") main()
def test_ddpg_popart(): """ Test DDPG with pop-art normalization """ n_actions = 1 action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions)) model = DDPG('MlpPolicy', 'Pendulum-v0', memory_limit=50000, normalize_observations=True, normalize_returns=True, nb_rollout_steps=128, nb_train_steps=1, batch_size=64, action_noise=action_noise, enable_popart=True) model.learn(1000)
def ppo1_nmileg_pool(sensory_value): RL_method = "PPO1" # total_MC_runs = 50 experiment_ID = "handtest_rot_pool_with_MC_C_task0/" save_name_extension = RL_method total_timesteps = 500000 sensory_info = "sensory_{}".format(sensory_value) current_mc_run_num =22 #starts from 0 for mc_cntr in range(current_mc_run_num, current_mc_run_num+1): log_dir = "./logs/{}/MC_{}/{}/{}/".format(experiment_ID, mc_cntr, RL_method, sensory_info) # defining the environments env = gym.make('HandManipulate-v1{}'.format(sensory_value)) #env = gym.wrappers.Monitor(env, "./tmp/gym-results", video_callable=False, force=True) ## setting the Monitor env = gym.wrappers.Monitor(env, log_dir+"Monitor/", video_callable=False, force=True, uid="Monitor_info") # defining the initial model if RL_method == "PPO1": model = PPO1(common_MlpPolicy, env, verbose=1, tensorboard_log=log_dir) elif RL_method == "PPO2": env = DummyVecEnv([lambda: env]) model = PPO2(common_MlpPolicy, env, verbose=1, tensorboard_log=log_dir) elif RL_method == "DDPG": env = DummyVecEnv([lambda: env]) n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5)* 5 * np.ones(n_actions)) model = DDPG(DDPG_MlpPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise, tensorboard_log=log_dir) else: raise ValueError("Invalid RL mode") # setting the environment on the model #model.set_env(env) # setting the random seed for some of the random instances random_seed = mc_cntr random.seed(random_seed) env.seed(random_seed) env.action_space.seed(random_seed) np.random.seed(random_seed) tf.random.set_random_seed(random_seed) # training the model # training the model model.learn(total_timesteps=total_timesteps) # saving the trained model model.save(log_dir+"/model") return None
def train_identity_ddpg(): env = DummyVecEnv([lambda: IdentityEnvBox(eps = 0.5)]) std = 0.2 param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(std), desired_action_stddev=float(std)) model = DDPG("MlpPolicy", env, gamma=0.0, param_noise=param_noise, memory_limit=int(1e6)) model.learn(total_timesteps=20000, seed=0) n_trials = 1000 reward_sum = 0 set_global_seeds(0) obs = env.reset() for _ in range(n_trials): action, _ = model.predict(obs) obs, reward, _, _ = env.step(action) reward_sum += reward assert reward_sum > 0.9 * n_trials del model, env
def train_DDPG(self, model_name, ddpg_params=config.DDPG_PARAMS): """DDPG model""" from stable_baselines import DDPG from stable_baselines.ddpg.policies import DDPGPolicy from stable_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise, AdaptiveParamNoiseSpec env_train = self.env start = time.time() model = DDPG('MlpPolicy', env_train, batch_size=ddpg_params['batch_size'], buffer_size=ddpg_params['buffer_size'], verbose=ddpg_params['verbose']) model.learn(total_timesteps=ddpg_params['timesteps']) end = time.time() model.save(f"{config.TRAINED_MODEL_DIR}/{model_name}") print('Training time (DDPG): ', (end - start) / 60, ' minutes') return model
def train_policy(num_of_envs, log_relative_path, maximum_episode_length, skip_frame, seed_num, ddpg_config, total_time_steps, validate_every_timesteps, task_name): print("Using MPI for multiprocessing with {} workers".format( MPI.COMM_WORLD.Get_size())) rank = MPI.COMM_WORLD.Get_rank() print("Worker rank: {}".format(rank)) task = generate_task(task_generator_id=task_name, dense_reward_weights=np.array( [250, 0, 125, 0, 750, 0, 0, 0.005]), fractional_reward_weight=1, goal_height=0.15, tool_block_mass=0.02) env = CausalWorld(task=task, skip_frame=skip_frame, enable_visualization=False, seed=0, max_episode_length=maximum_episode_length, normalize_actions=False, normalize_observations=False) n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) policy_kwargs = dict(layers=[256, 256]) checkpoint_callback = CheckpointCallback(save_freq=int( validate_every_timesteps / num_of_envs), save_path=log_relative_path, name_prefix='model') model = DDPG(MlpPolicy, env, verbose=2, param_noise=param_noise, action_noise=action_noise, policy_kwargs=policy_kwargs, **ddpg_config) model.learn(total_timesteps=total_time_steps, tb_log_name="ddpg", callback=checkpoint_callback) return
def run_baseline_ddpg(env_name, train=True): import numpy as np # from stable_baselines.ddpg.policies import MlpPolicy from stable_baselines.common.vec_env import DummyVecEnv from stable_baselines.ddpg.noise import OrnsteinUhlenbeckActionNoise from stable_baselines import DDPG env = gym.make(env_name) env = DummyVecEnv([lambda: env]) if train: # mlp from stable_baselines.ddpg.policies import FeedForwardPolicy class CustomPolicy(FeedForwardPolicy): def __init__(self, *args, **kwargs): super(CustomPolicy, self).__init__(*args, **kwargs, layers=[64, 64, 64], layer_norm=True, feature_extraction="mlp") # the noise objects for DDPG n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions)+0.15, sigma=0.3 * np.ones(n_actions)) model = DDPG(CustomPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise, tau=0.01, observation_range=(env.observation_space.low, env.observation_space.high), critic_l2_reg=0, actor_lr=1e-3, critic_lr=1e-3, memory_limit=100000) model.learn(total_timesteps=1e5) model.save("checkpoints/ddpg_" + env_name) else: model = DDPG.load("checkpoints/ddpg_" + env_name) obs = env.reset() while True: action, _states = model.predict(obs) obs, rewards, dones, info = env.step(action) env.render() print("state: ", obs, " reward: ", rewards, " done: ", dones, "info: ", info) del model # remove to demonstrate saving and loading
def main(args): #Starting the timer to record the operation time. start = time.time() env_id = 'fwmav_hover-v0' #Creating a vector of size 1 which only has the environment. env = DummyVecEnv([make_env(env_id, 0)]) # env = SubprocVecEnv([make_env(env_id, i) for i in range(args.n_cpu)]) # -1 argument means the shape will be found automatically. n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) model = DDPG( policy=MyDDPGPolicy, env=env, gamma=1.0, nb_train_steps=5000, nb_rollout_steps=10000, nb_eval_steps=10000, param_noise=param_noise, action_noise=action_noise, tau=0.003, batch_size=256, observation_range=(-np.inf, np.inf), actor_lr=0.0001, critic_lr=0.001, reward_scale=0.05, memory_limit=10000000, verbose=1, ) model.learn(total_timesteps=args.time_step) model.save(args.model_path) #End timer. end = time.time() print("Time used: ", end - start)
def training(env): n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions)) model = DDPG(MlpPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise, render=True, return_range=[-1.0, 1.0], observation_range=[-2.0, 2.0]) model.learn(total_timesteps=40000) time = datetime.now().strftime("%m%d_%H%M%S") model.save("models\\ddpg_sbl_" + time) del model # remove to demonstrate saving and loading testing(env, time)
def ppo1_nmileg_pool(stiffness_value): RL_method = "PPO1" experiment_ID = "experiment_4_pool_A/mc_1/" save_name_extension = RL_method total_timesteps = 500000 stiffness_value_str = "stiffness_{}".format(stiffness_value) log_dir = "./logs/{}/{}/{}/".format(experiment_ID, RL_method, stiffness_value_str) # defining the environments env = gym.make('TSNMILeg{}-v1'.format(stiffness_value)) #env = gym.wrappers.Monitor(env, "./tmp/gym-results", video_callable=False, force=True) # defining the initial model if RL_method == "PPO1": model = PPO1(common_MlpPolicy, env, verbose=1, tensorboard_log=log_dir) elif RL_method == "PPO2": env = DummyVecEnv([lambda: env]) model = PPO2(common_MlpPolicy, env, verbose=1, tensorboard_log=log_dir) elif RL_method == "DDPG": env = DummyVecEnv([lambda: env]) n_actions = env.action_space.shape[-1] param_noise = None action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * 5 * np.ones(n_actions)) model = DDPG(DDPG_MlpPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise, tensorboard_log=log_dir) else: raise ValueError("Invalid RL mode") # setting the environment on the model #model.set_env(env) # training the model # training the model model.learn(total_timesteps=total_timesteps) # saving the trained model model.save(log_dir + "/model") return None
def optimize_agent(trial): """ Train the model and optimise Optuna maximises the negative log likelihood, so we need to negate the reward here """ model_params = optimize_ddpg(trial) seed = trial.suggest_int('numpyseed', 1, 429496729) np.random.seed(seed) original_env = gym.make('rustyblocks-v0') original_env.max_invalid_tries = 3 env = DummyVecEnv([lambda: original_env]) model = DDPG("MlpPolicy", env, verbose=0, observation_range=(-126,126), **model_params) print("DOING LEARING a2c") original_env.force_progression = False model.learn(int(2e4*5), seed=seed) print("DONE LEARING a2c") original_env.max_invalid_tries = -1 rewards = [] n_episodes, reward_sum = 0, 0.0 obs = env.reset() original_env.force_progression = True original_env.invalid_try_limit = 5000 while n_episodes < 4: action, _ = model.predict(obs) obs, reward, done, _ = env.step(action) reward_sum += reward if done: rewards.append(reward_sum) reward_sum = 0.0 n_episodes += 1 obs = env.reset() last_reward = np.mean(rewards) trial.report(last_reward) return last_reward