Add batch RL example notebook (#926)

reinforcement_learning/common/env_utils.py

@@ -0,0 +1,211 @@
+import gym
+import numpy as np
+import pandas as pd
+import json
+from pathlib import Path
+
+gym.logger.set_level(40)
+
+class VectoredGymEnvironment():
+    """
+    Envrioment class to run multiple similations and collect rollout data
+    """
+    def __init__(self, registered_gym_env, num_of_envs=1):
+        self.envs_initialized = False
+        self.initialized_envs = {}
+        self.env_states = {}
+        self.env_reset_counter = {}
+        self.num_of_envs = num_of_envs
+        self.data_rows = []
+
+        self.initialize_envs(num_of_envs, registered_gym_env)
+
+    def is_initialized(self):
+        return self.envs_initialized
+
+    def initialize_envs(
+            self,
+            num_of_envs,
+            registered_gym_env):
+        """Initialize multiple Openai gym environments.
+        Each envrionment will start with a different random seed.
+
+        Arguments:
+            num_of_envs {int} -- Number of environments/simulations to initiate
+            registered_gym_env {str} -- Environment name of the registered gym environment
+        """
+        print("Initializing {} environments of {}".format(num_of_envs, registered_gym_env))
+        for i in range(0, num_of_envs):
+            environment_id = "environment_" + str(i)
+            environment = gym.make(registered_gym_env)
+            environment = environment.unwrapped
+            environment.seed(i)
+            self.env_states[environment_id] = environment.reset()
+            self.env_reset_counter[environment_id] = 0
+            self.initialized_envs[environment_id] = environment
+        self.envs_initialized = True
+        self.state_dims = len(self.env_states[environment_id])
+
+    def get_environment_states(self):
+        return self.env_states
+
+    def dump_environment_states(self, dir_path, file_name):
+        """Dumping current states of all the envrionments into file
+        
+        Arguments:
+            dir_path {str} -- Directory path of the target file
+            file_name {str} -- File name of the target file
+        """
+        data_folder = Path(dir_path)
+        file_path = data_folder / file_name
+
+        with open(file_path, 'w') as outfile:
+            for state in self.env_states.values():
+                json.dump(list(state), outfile)
+                outfile.write('\n')
+
+    def get_environment_ids(self):
+        return list(self.initialized_envs.keys())
+
+    def step(self, environment_id, action):
+        local_env = self.initialized_envs[environment_id]
+        observation, reward, done, info = local_env.step(action)
+
+        self.env_states[environment_id] = observation
+        return observation, reward, done, info
+
+    def reset(self, environment_id):
+        self.env_states[environment_id] = \
+            self.initialized_envs[environment_id].reset()
+        return self.env_states[environment_id]
+
+    def reset_all_envs(self):
+        print("Resetting all the environments...")
+        for i in range(0, self.num_of_envs): 
+            environment_id = "environment_" + str(i)
+            self.reset(environment_id)
+
+    def close(self, environment_id):
+        self.initialized_envs[environment_id].close()
+        return
+
+    def render(self, environment_id):
+        self.initialized_envs[environment_id].render()
+        return
+
+    def collect_rollouts_for_single_env_with_given_episodes(self, environment_id, action_prob, num_episodes):
+        """Collect rollouts with given steps from one environment
+        
+        Arguments:
+            environment_id {str} -- Environment id for the environment
+            action_prob {list} -- Action probabilities of the simulated policy
+            num_episodes {int} -- Number of episodes to run rollouts
+        """
+        # normalization if sum of probs is not exact equal to 1
+        action_prob = np.array(action_prob)
+        if action_prob.sum() != 1:
+            action_prob /= action_prob.sum()
+        action_prob = list(action_prob)
+
+        for _ in range(num_episodes):
+            done = False
+            cumulative_rewards = 0
+            while not done:
+                data_item = []
+                action = np.random.choice(len(action_prob), p=action_prob)
+                cur_state_features = self.env_states[environment_id]
+                _, reward, done, _ = self.step(environment_id, action)
+                cumulative_rewards += reward
+                episode_id = int(environment_id.split('_')[-1]) + \
+                    self.num_of_envs * self.env_reset_counter[environment_id]
+                if not done:
+                    data_item.extend([action, action_prob, episode_id, reward, 0.0])
+                else:
+                    data_item.extend([action, action_prob, episode_id, reward, cumulative_rewards])
+                for j in range(len(cur_state_features)):
+                    data_item.append(cur_state_features[j])
+                self.data_rows.append(data_item)
+
+            self.reset(environment_id)
+            self.env_reset_counter[environment_id] += 1
+
+    def collect_rollouts_for_single_env_with_given_steps(self, environment_id, action_prob, num_steps):
+        """Collect rollouts with given steps from one environment
+        
+        Arguments:
+            environment_id {str} -- Environment id for the environment
+            action_prob {list} -- Action probabilities of the simulated policy
+            num_episodes {int} -- Number of steps to run rollouts
+        """
+        # normalization if sum of probs is not exact equal to 1
+        action_prob = np.array(action_prob)
+        if action_prob.sum() != 1:
+            action_prob /= action_prob.sum()
+        action_prob = list(action_prob)
+
+        for _ in range(num_steps):
+            data_item = []
+            action = np.random.choice(len(action_prob), p=action_prob)
+            cur_state_features = self.env_states[environment_id]
+            _, reward, done, _ = self.step(environment_id, action)
+            episode_id = int(environment_id.split('_')[-1]) + \
+                self.num_of_envs * self.env_reset_counter[environment_id]
+            data_item.extend([action, action_prob, episode_id, reward])
+            for j in range(len(cur_state_features)):
+                data_item.append(cur_state_features[j])
+            self.data_rows.append(data_item)
+            if done:
+                self.reset(environment_id)
+                self.env_reset_counter[environment_id] += 1
+
+    def collect_rollouts_with_given_action_probs(self, num_steps=None, num_episodes=None, action_probs=None, file_name=None):
+        """Collect rollouts from all the initiated environments with given action probs
+        
+        Keyword Arguments:
+            num_steps {int} -- Number of steps to run rollouts (default: {None})
+            num_episodes {int} --  Number of episodes to run rollouts (default: {None})
+            action_probs {list} -- Action probs for the policy (default: {None})
+            file_name {str} -- Batch transform output that contain predictions of probs  (default: {None})
+        
+        Returns:
+            [Dataframe] -- Dataframe that contains the rollout data from all envs
+        """
+        if file_name is not None:
+            assert action_probs is None
+            json_lines = [json.loads(line.rstrip('\n')) for line in open(file_name) if line is not '']
+            action_probs = []
+            for line in json_lines:
+                if line.get('SageMakerOutput') is not None:
+                    action_probs.append(line['SageMakerOutput'].get("predictions")[0])
+                else:
+                    action_probs.append(line.get("predictions")[0])
+
+        assert len(action_probs) == self.num_of_envs
+        for index, environment_id in enumerate(self.get_environment_ids()):
+            if num_steps is not None:
+                assert num_episodes is None
+                self.collect_rollouts_for_single_env_with_given_steps(
+                    environment_id, action_probs[index], num_steps
+                )
+            else:
+                assert num_episodes is not None
+                self.collect_rollouts_for_single_env_with_given_episodes(
+                    environment_id, action_probs[index], num_episodes
+                )
+
+        col_names = self._create_col_names()
+        df = pd.DataFrame(self.data_rows, columns = col_names)
+
+        return df
+
+    def _create_col_names(self):
+        """Create column names of dataframe that can be consumed by Coach
+        
+        Returns:
+            [list] -- List of column names
+        """
+        col_names = ['action', 'all_action_probabilities', 'episode_id', 'reward', 'cumulative_rewards']
+        for i in range(self.state_dims):
+            col_names.append('state_feature_' + str(i))
+
+        return col_names

reinforcement_learning/rl_cartpole_batch_coach/README.md

@@ -0,0 +1,14 @@
+# Training Batch Reinforcement Learning Policies with Amazon SageMaker RL
+
+In many real-world problems, the reinforcement learning agent cannot interact with neither the real environment nor a simulated one. On one hand, creating a simulator that imitates the real environment dynamic could be quite complex and on the other, letting the learning agent attempt sub-optimal actions in the real world is quite risky. In such cases, the learning agent can only have access to batches of offline data that generated by some deployed policy. The learning agent need to utilize these data correctly to learn a better policy to solve the problem.
+
+This notebook shows an example of how to use batch reinforcement learning techniques to address such type of real-world problems: training a new policy from offline dataset when there is no way to interact with real environments or simulators. This example is a simple toy demonstrating how one might begin to address this real and challenging problem. We use gym `CartPole-v0` as a fake simulated system to generate offline dataset and the RL agents are trained using Amazon SageMaker RL.
+
+## Contents
+
+* `rl_cartpole_batch_coach.ipynb`: notebook used for training policy with Batch RL to solve the cartpole problem.
+* `src/`
+  * `train-coach.py`: launcher for coach training.
+  * `evaluate-coach.py`: launcher for coach evaluation.
+  * `preset-cartpole-ddqnbcq.py`: coach preset for BCQ algorithm.
+  * `preset-cartpole-ddqnbcq-env.py`: coach preset for BCQ algorithm with environment setup.

reinforcement_learning/rl_cartpole_batch_coach/common

@@ -0,0 +1 @@
+../common