def run_task(snapshot_config, *_): """Set up environment and algorithm and run the task. Args: snapshot_config (metarl.experiment.SnapshotConfig): The snapshot configuration used by LocalRunner to create the snapshotter. If None, it will create one with default settings. _ : Unused parameters """ # create multi-task environment and sample tasks env_sampler = SetTaskSampler( lambda: MetaRLEnv(normalize(ML1.get_train_tasks('push-v1')))) env = env_sampler.sample(params['num_train_tasks']) test_env_sampler = SetTaskSampler( lambda: MetaRLEnv(normalize(ML1.get_test_tasks('push-v1')))) test_env = test_env_sampler.sample(params['num_train_tasks']) runner = LocalRunner(snapshot_config) obs_dim = int(np.prod(env[0]().observation_space.shape)) action_dim = int(np.prod(env[0]().action_space.shape)) reward_dim = 1 # instantiate networks encoder_in_dim = obs_dim + action_dim + reward_dim encoder_out_dim = params['latent_size'] * 2 net_size = params['net_size'] context_encoder = MLPEncoder(input_dim=encoder_in_dim, output_dim=encoder_out_dim, hidden_sizes=[200, 200, 200]) space_a = akro.Box(low=-1, high=1, shape=(obs_dim + params['latent_size'], ), dtype=np.float32) space_b = akro.Box(low=-1, high=1, shape=(action_dim, ), dtype=np.float32) augmented_env = EnvSpec(space_a, space_b) qf1 = ContinuousMLPQFunction(env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size]) qf2 = ContinuousMLPQFunction(env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size]) obs_space = akro.Box(low=-1, high=1, shape=(obs_dim, ), dtype=np.float32) action_space = akro.Box(low=-1, high=1, shape=(params['latent_size'], ), dtype=np.float32) vf_env = EnvSpec(obs_space, action_space) vf = ContinuousMLPQFunction(env_spec=vf_env, hidden_sizes=[net_size, net_size, net_size]) policy = TanhGaussianMLPPolicy2( env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size]) context_conditioned_policy = ContextConditionedPolicy( latent_dim=params['latent_size'], context_encoder=context_encoder, policy=policy, use_ib=params['use_information_bottleneck'], use_next_obs=params['use_next_obs_in_context'], ) pearlsac = PEARLSAC( env=env, test_env=test_env, policy=context_conditioned_policy, qf1=qf1, qf2=qf2, vf=vf, num_train_tasks=params['num_train_tasks'], num_test_tasks=params['num_test_tasks'], latent_dim=params['latent_size'], meta_batch_size=params['meta_batch_size'], num_steps_per_epoch=params['num_steps_per_epoch'], num_initial_steps=params['num_initial_steps'], num_tasks_sample=params['num_tasks_sample'], num_steps_prior=params['num_steps_prior'], num_extra_rl_steps_posterior=params['num_extra_rl_steps_posterior'], num_evals=params['num_evals'], num_steps_per_eval=params['num_steps_per_eval'], batch_size=params['batch_size'], embedding_batch_size=params['embedding_batch_size'], embedding_mini_batch_size=params['embedding_mini_batch_size'], max_path_length=params['max_path_length'], reward_scale=params['reward_scale'], ) tu.set_gpu_mode(params['use_gpu'], gpu_id=0) if params['use_gpu']: pearlsac.to() runner.setup(algo=pearlsac, env=env, sampler_cls=PEARLSampler, sampler_args=dict(max_path_length=params['max_path_length'])) runner.train(n_epochs=params['num_epochs'], batch_size=params['batch_size'])
def run_metarl(env, test_env, seed, log_dir): """Create metarl model and training.""" deterministic.set_seed(seed) snapshot_config = SnapshotConfig(snapshot_dir=log_dir, snapshot_mode='gap', snapshot_gap=10) runner = LocalRunner(snapshot_config) obs_dim = int(np.prod(env[0]().observation_space.shape)) action_dim = int(np.prod(env[0]().action_space.shape)) reward_dim = 1 # instantiate networks encoder_in_dim = obs_dim + action_dim + reward_dim encoder_out_dim = params['latent_size'] * 2 net_size = params['net_size'] context_encoder = MLPEncoder(input_dim=encoder_in_dim, output_dim=encoder_out_dim, hidden_sizes=[200, 200, 200]) space_a = akro.Box(low=-1, high=1, shape=(obs_dim + params['latent_size'], ), dtype=np.float32) space_b = akro.Box(low=-1, high=1, shape=(action_dim, ), dtype=np.float32) augmented_env = EnvSpec(space_a, space_b) qf1 = ContinuousMLPQFunction(env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size]) qf2 = ContinuousMLPQFunction(env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size]) obs_space = akro.Box(low=-1, high=1, shape=(obs_dim, ), dtype=np.float32) action_space = akro.Box(low=-1, high=1, shape=(params['latent_size'], ), dtype=np.float32) vf_env = EnvSpec(obs_space, action_space) vf = ContinuousMLPQFunction(env_spec=vf_env, hidden_sizes=[net_size, net_size, net_size]) policy = TanhGaussianMLPPolicy2( env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size]) context_conditioned_policy = ContextConditionedPolicy( latent_dim=params['latent_size'], context_encoder=context_encoder, policy=policy, use_ib=params['use_information_bottleneck'], use_next_obs=params['use_next_obs_in_context'], ) train_task_names = ML10.get_train_tasks()._task_names test_task_names = ML10.get_test_tasks()._task_names pearlsac = PEARLSAC( env=env, test_env=test_env, policy=context_conditioned_policy, qf1=qf1, qf2=qf2, vf=vf, num_train_tasks=params['num_train_tasks'], num_test_tasks=params['num_test_tasks'], latent_dim=params['latent_size'], meta_batch_size=params['meta_batch_size'], num_steps_per_epoch=params['num_steps_per_epoch'], num_initial_steps=params['num_initial_steps'], num_tasks_sample=params['num_tasks_sample'], num_steps_prior=params['num_steps_prior'], num_extra_rl_steps_posterior=params['num_extra_rl_steps_posterior'], num_evals=params['num_evals'], num_steps_per_eval=params['num_steps_per_eval'], batch_size=params['batch_size'], embedding_batch_size=params['embedding_batch_size'], embedding_mini_batch_size=params['embedding_mini_batch_size'], max_path_length=params['max_path_length'], reward_scale=params['reward_scale'], train_task_names=train_task_names, test_task_names=test_task_names, ) tu.set_gpu_mode(params['use_gpu'], gpu_id=0) if params['use_gpu']: pearlsac.to() tabular_log_file = osp.join(log_dir, 'progress.csv') tensorboard_log_dir = osp.join(log_dir) dowel_logger.add_output(dowel.StdOutput()) dowel_logger.add_output(dowel.CsvOutput(tabular_log_file)) dowel_logger.add_output(dowel.TensorBoardOutput(tensorboard_log_dir)) runner.setup(algo=pearlsac, env=env, sampler_cls=PEARLSampler, sampler_args=dict(max_path_length=params['max_path_length'])) runner.train(n_epochs=params['num_epochs'], batch_size=params['batch_size']) dowel_logger.remove_all() return tabular_log_file