def rl2_ppo_metaworld_ml1_push(ctxt, seed, max_path_length, meta_batch_size, n_epochs, episode_per_task): """Train PPO with ML1 environment. Args: ctxt (metarl.experiment.ExperimentContext): The experiment configuration used by LocalRunner to create the snapshotter. seed (int): Used to seed the random number generator to produce determinism. max_path_length (int): Maximum length of a single rollout. meta_batch_size (int): Meta batch size. n_epochs (int): Total number of epochs for training. episode_per_task (int): Number of training episode per task. """ set_seed(seed) with LocalTFRunner(snapshot_config=ctxt) as runner: tasks = task_sampler.SetTaskSampler(lambda: RL2Env( env=mwb.ML1.get_train_tasks('push-v1'))) env_spec = RL2Env(env=mwb.ML1.get_train_tasks('push-v1')).spec policy = GaussianGRUPolicy(name='policy', hidden_dim=64, env_spec=env_spec, state_include_action=False) baseline = LinearFeatureBaseline(env_spec=env_spec) algo = RL2PPO(rl2_max_path_length=max_path_length, meta_batch_size=meta_batch_size, task_sampler=tasks, env_spec=env_spec, policy=policy, baseline=baseline, discount=0.99, gae_lambda=0.95, lr_clip_range=0.2, optimizer_args=dict( batch_size=32, max_epochs=10, ), stop_entropy_gradient=True, entropy_method='max', policy_ent_coeff=0.02, center_adv=False, max_path_length=max_path_length * episode_per_task) runner.setup(algo, tasks.sample(meta_batch_size), sampler_cls=LocalSampler, n_workers=meta_batch_size, worker_class=RL2Worker, worker_args=dict(n_paths_per_trial=episode_per_task)) runner.train(n_epochs=n_epochs, batch_size=episode_per_task * max_path_length * meta_batch_size)
def setup_method(self): super().setup_method() self.max_path_length = 100 self.meta_batch_size = 10 self.episode_per_task = 4 self.tasks = task_sampler.SetTaskSampler( lambda: RL2Env(env=normalize(HalfCheetahDirEnv()))) self.env_spec = RL2Env(env=normalize(HalfCheetahDirEnv())).spec self.policy = GaussianGRUPolicy(env_spec=self.env_spec, hidden_dim=64, state_include_action=False) self.baseline = LinearFeatureBaseline(env_spec=self.env_spec)
def rl2_trpo_halfcheetah(ctxt, seed, max_path_length, meta_batch_size, n_epochs, episode_per_task): """Train TRPO with HalfCheetah environment. Args: ctxt (metarl.experiment.ExperimentContext): The experiment configuration used by LocalRunner to create the snapshotter. seed (int): Used to seed the random number generator to produce determinism. max_path_length (int): Maximum length of a single rollout. meta_batch_size (int): Meta batch size. n_epochs (int): Total number of epochs for training. episode_per_task (int): Number of training episode per task. """ set_seed(seed) with LocalTFRunner(snapshot_config=ctxt) as runner: tasks = task_sampler.SetTaskSampler( lambda: RL2Env(env=HalfCheetahVelEnv())) env_spec = RL2Env(env=HalfCheetahVelEnv()).spec policy = GaussianGRUPolicy(name='policy', hidden_dim=64, env_spec=env_spec, state_include_action=False) baseline = LinearFeatureBaseline(env_spec=env_spec) algo = RL2TRPO(rl2_max_path_length=max_path_length, meta_batch_size=meta_batch_size, task_sampler=tasks, env_spec=env_spec, policy=policy, baseline=baseline, max_path_length=max_path_length * episode_per_task, discount=0.99, max_kl_step=0.01, optimizer=ConjugateGradientOptimizer, optimizer_args=dict(hvp_approach=FiniteDifferenceHvp( base_eps=1e-5))) runner.setup(algo, tasks.sample(meta_batch_size), sampler_cls=LocalSampler, n_workers=meta_batch_size, worker_class=RL2Worker, worker_args=dict(n_paths_per_trial=episode_per_task)) runner.train(n_epochs=n_epochs, batch_size=episode_per_task * max_path_length * meta_batch_size)
def test_observation_dimension(self): env = PointEnv() wrapped_env = RL2Env(PointEnv()) assert wrapped_env.spec.observation_space.shape[0] == ( env.observation_space.shape[0] + env.action_space.shape[0] + 2) obs = env.reset() obs2 = wrapped_env.reset() assert obs.shape[0] + env.action_space.shape[0] + 2 == obs2.shape[0] obs, _, _, _ = env.step(env.action_space.sample()) obs2, _, _, _ = wrapped_env.step(env.action_space.sample()) assert obs.shape[0] + env.action_space.shape[0] + 2 == obs2.shape[0]
def rl2_ppo_metaworld_ml10_meta_test(ctxt, seed, max_path_length, meta_batch_size, n_epochs, episode_per_task): """Train PPO with ML10 environment with meta-test. Args: ctxt (metarl.experiment.ExperimentContext): The experiment configuration used by LocalRunner to create the snapshotter. seed (int): Used to seed the random number generator to produce determinism. max_path_length (int): Maximum length of a single rollout. meta_batch_size (int): Meta batch size. n_epochs (int): Total number of epochs for training. episode_per_task (int): Number of training episode per task. """ set_seed(seed) with LocalTFRunner(snapshot_config=ctxt) as runner: ml10_train_envs = [ RL2Env(mwb.ML10.from_task(task_name)) for task_name in mwb.ML10.get_train_tasks().all_task_names ] tasks = task_sampler.EnvPoolSampler(ml10_train_envs) tasks.grow_pool(meta_batch_size) ml10_test_envs = [ RL2Env(mwb.ML10.from_task(task_name)) for task_name in mwb.ML10.get_test_tasks().all_task_names ] test_tasks = task_sampler.EnvPoolSampler(ml10_test_envs) env_spec = ml10_train_envs[0].spec policy = GaussianGRUPolicy(name='policy', hidden_dim=64, env_spec=env_spec, state_include_action=False) baseline = LinearFeatureBaseline(env_spec=env_spec) meta_evaluator = MetaEvaluator(test_task_sampler=test_tasks, n_exploration_traj=10, n_test_rollouts=10, max_path_length=max_path_length, n_test_tasks=5) algo = RL2PPO(rl2_max_path_length=max_path_length, meta_batch_size=meta_batch_size, task_sampler=tasks, env_spec=env_spec, policy=policy, baseline=baseline, discount=0.99, gae_lambda=0.95, lr_clip_range=0.2, optimizer_args=dict( batch_size=32, max_epochs=10, ), stop_entropy_gradient=True, entropy_method='max', policy_ent_coeff=0.02, center_adv=False, max_path_length=max_path_length * episode_per_task, meta_evaluator=meta_evaluator, n_epochs_per_eval=10) runner.setup(algo, tasks.sample(meta_batch_size), sampler_cls=LocalSampler, n_workers=meta_batch_size, worker_class=RL2Worker, worker_args=dict(n_paths_per_trial=episode_per_task)) runner.train(n_epochs=n_epochs, batch_size=episode_per_task * max_path_length * meta_batch_size)