format using yapf

backends/baselines_learner.py

@@ -35,7 +35,9 @@ class PPO2Agent(LearnerBase):
 
         self.log_path = log_path
 
-        self.env = DummyVecEnv([lambda: env]) #PPO2 requried a vectorized environment for parallel training
+        self.env = DummyVecEnv([
+            lambda: env
+        ])  #PPO2 requried a vectorized environment for parallel training
         self.agent_model = self.create_agent(policy, tensorboard)
 
     def get_default_policy(self):
@@ -52,7 +54,8 @@ class PPO2Agent(LearnerBase):
             stable_baselines PPO2 object
         """
         if tensorboard:
-            return PPO2(policy, self.env, verbose=1, tensorboard_log=self.log_path)
+            return PPO2(
+                policy, self.env, verbose=1, tensorboard_log=self.log_path)
         else:
             return PPO2(policy, self.env, verbose=1)
 
@@ -100,7 +103,8 @@ class PPO2Agent(LearnerBase):
             episode_rewards[-1] += rewards[0]
             if dones[0] or current_step > nb_max_episode_steps:
                 obs = self.env.reset()
-                print ("Episode ", current_episode, "reward: ", episode_rewards[-1])
+                print("Episode ", current_episode, "reward: ",
+                      episode_rewards[-1])
                 episode_rewards.append(0.0)
                 current_episode += 1
                 current_step = 0

backends/controller_base.py

@@ -50,11 +50,13 @@ class ControllerBase(PolicyBase):
     
     Returns state at end of node execution, total reward, epsiode_termination_flag, info
     '''
+
     def step_current_node(self, visualize_low_level_steps=False):
         total_reward = 0
         self.node_terminal_state_reached = False
         while not self.node_terminal_state_reached:
-            observation, reward, terminal, info = self.low_level_step_current_node()
+            observation, reward, terminal, info = self.low_level_step_current_node(
+            )
             if visualize_low_level_steps:
                 self.env.render()
             total_reward += reward
@@ -70,9 +72,12 @@ class ControllerBase(PolicyBase):
 
     Returns state after one step, step reward, episode_termination_flag, info
     '''
+
     def low_level_step_current_node(self):
 
-        u_ego = self.current_node.low_level_policy(self.current_node.get_reduced_features_tuple())
+        u_ego = self.current_node.low_level_policy(
+            self.current_node.get_reduced_features_tuple())
         feature, R, terminal, info = self.current_node.step(u_ego)
         self.node_terminal_state_reached = terminal
-        return self.env.get_features_tuple(), R, self.env.termination_condition, info
+        return self.env.get_features_tuple(
+        ), R, self.env.termination_condition, info

backends/kerasrl_learner.py

@@ -47,7 +47,8 @@ class DDPGLearner(LearnerBase):
             "oup_mu": 0,  # OrnsteinUhlenbeckProcess mu
             "oup_sigma": 1,  # OrnsteinUhlenbeckProcess sigma
             "oup_sigma_min": 0.5,  # OrnsteinUhlenbeckProcess sigma min
-            "oup_annealing_steps": 500000, # OrnsteinUhlenbeckProcess n-step annealing
+            "oup_annealing_steps":
+            500000,  # OrnsteinUhlenbeckProcess n-step annealing
             "nb_steps_warmup_critic": 100,  # steps for critic to warmup
             "nb_steps_warmup_actor": 100,  # steps for actor to warmup
             "target_model_update": 1e-3  # target model update frequency
@@ -160,24 +161,33 @@ class DDPGLearner(LearnerBase):
             target_model_update=1e-3)
 
         # TODO: give params like lr_actor and lr_critic to set different lr of Actor and Critic.
-        agent.compile([Adam(lr=self.lr*1e-2, clipnorm=1.), Adam(lr=self.lr, clipnorm=1.)], metrics=['mae'])
+        agent.compile(
+            [
+                Adam(lr=self.lr * 1e-2, clipnorm=1.),
+                Adam(lr=self.lr, clipnorm=1.)
+            ],
+            metrics=['mae'])
 
         return agent
 
     def train(self,
-            env,
-            nb_steps=1000000,
-            visualize=False,
-            verbose=1,
-            log_interval=10000,
-            nb_max_episode_steps=200,
-            model_checkpoints=False,
-            checkpoint_interval=100000,
-            tensorboard=False):
+              env,
+              nb_steps=1000000,
+              visualize=False,
+              verbose=1,
+              log_interval=10000,
+              nb_max_episode_steps=200,
+              model_checkpoints=False,
+              checkpoint_interval=100000,
+              tensorboard=False):
 
         callbacks = []
         if model_checkpoints:
-            callbacks += [ModelIntervalCheckpoint('./checkpoints/checkpoint_weights.h5f', interval=checkpoint_interval)]
+            callbacks += [
+                ModelIntervalCheckpoint(
+                    './checkpoints/checkpoint_weights.h5f',
+                    interval=checkpoint_interval)
+            ]
         if tensorboard:
             callbacks += [TensorBoard(log_dir='./logs')]
 
@@ -291,28 +301,36 @@ class DQNLearner(LearnerBase):
         Returns:
             KerasRL DQN object
         """
-        agent = DQNAgentOverOptions(model=model, low_level_policies=self.low_level_policies,
-                                         nb_actions=self.nb_actions, memory=memory,
-                                         nb_steps_warmup=self.nb_steps_warmup, target_model_update=self.target_model_update,
-                                         policy=policy, enable_dueling_network=True)
-
+        agent = DQNAgentOverOptions(
+            model=model,
+            low_level_policies=self.low_level_policies,
+            nb_actions=self.nb_actions,
+            memory=memory,
+            nb_steps_warmup=self.nb_steps_warmup,
+            target_model_update=self.target_model_update,
+            policy=policy,
+            enable_dueling_network=True)
 
         agent.compile(Adam(lr=self.lr), metrics=['mae'])
 
         return agent
 
     def train(self,
-            env,
-            nb_steps=1000000,
-            visualize=False,
-            nb_max_episode_steps=200,
-            tensorboard=False,
-            model_checkpoints=False,
-            checkpoint_interval=10000):
+              env,
+              nb_steps=1000000,
+              visualize=False,
+              nb_max_episode_steps=200,
+              tensorboard=False,
+              model_checkpoints=False,
+              checkpoint_interval=10000):
 
         callbacks = []
         if model_checkpoints:
-            callbacks += [ModelIntervalCheckpoint('./checkpoints/checkpoint_weights.h5f', interval=checkpoint_interval)]
+            callbacks += [
+                ModelIntervalCheckpoint(
+                    './checkpoints/checkpoint_weights.h5f',
+                    interval=checkpoint_interval)
+            ]
         if tensorboard:
             callbacks += [TensorBoard(log_dir='./logs')]
 
@@ -333,7 +351,7 @@ class DQNLearner(LearnerBase):
                    nb_episodes=5,
                    visualize=True,
                    nb_max_episode_steps=400,
-                   success_reward_threshold = 100):
+                   success_reward_threshold=100):
 
         print("Testing for {} episodes".format(nb_episodes))
         success_count = 0
@@ -359,13 +377,14 @@ class DQNLearner(LearnerBase):
                     env.reset()
                     if episode_reward >= success_reward_threshold:
                         success_count += 1
-                    print("Episode {}: steps:{}, reward:{}".format(n+1, step, episode_reward))
+                    print("Episode {}: steps:{}, reward:{}".format(
+                        n + 1, step, episode_reward))
 
-        print ("\nPolicy succeeded {} times!".format(success_count))
-        print ("Failures due to:")
-        print (termination_reason_counter)
+        print("\nPolicy succeeded {} times!".format(success_count))
+        print("Failures due to:")
+        print(termination_reason_counter)
 
-        return [success_count,termination_reason_counter]
+        return [success_count, termination_reason_counter]
 
     def load_model(self, file_name="test_weights.h5f"):
         self.agent_model.load_weights(file_name)
@@ -377,27 +396,39 @@ class DQNLearner(LearnerBase):
         return self.agent_model.get_modified_q_values(observation)[action]
 
     def get_q_value_using_option_alias(self, observation, option_alias):
-        action_num = self.agent_model.low_level_policy_aliases.index(option_alias)
+        action_num = self.agent_model.low_level_policy_aliases.index(
+            option_alias)
         return self.agent_model.get_modified_q_values(observation)[action_num]
 
     def get_softq_value_using_option_alias(self, observation, option_alias):
-        action_num = self.agent_model.low_level_policy_aliases.index(option_alias)
+        action_num = self.agent_model.low_level_policy_aliases.index(
+            option_alias)
         q_values = self.agent_model.get_modified_q_values(observation)
         max_q_value = np.abs(np.max(q_values))
-        q_values = [np.exp(q_value/max_q_value) for q_value in q_values]
-        relevant = q_values[action_num]/np.sum(q_values)
+        q_values = [np.exp(q_value / max_q_value) for q_value in q_values]
+        relevant = q_values[action_num] / np.sum(q_values)
         return relevant
 
-class DQNAgentOverOptions(DQNAgent):
 
-    def __init__(self, model, low_level_policies, policy=None, test_policy=None, enable_double_dqn=True, enable_dueling_network=False,
-                 dueling_type='avg', *args, **kwargs):
-        super(DQNAgentOverOptions, self).__init__(model, policy, test_policy, enable_double_dqn, enable_dueling_network,
-                 dueling_type, *args, **kwargs)
+class DQNAgentOverOptions(DQNAgent):
+    def __init__(self,
+                 model,
+                 low_level_policies,
+                 policy=None,
+                 test_policy=None,
+                 enable_double_dqn=True,
+                 enable_dueling_network=False,
+                 dueling_type='avg',
+                 *args,
+                 **kwargs):
+        super(DQNAgentOverOptions, self).__init__(
+            model, policy, test_policy, enable_double_dqn,
+            enable_dueling_network, dueling_type, *args, **kwargs)
 
         self.low_level_policies = low_level_policies
         if low_level_policies is not None:
-            self.low_level_policy_aliases = list(self.low_level_policies.keys())
+            self.low_level_policy_aliases = list(
+                self.low_level_policies.keys())
 
     def __get_invalid_node_indices(self):
         """Returns a list of option indices that are invalid according to initiation conditions.
@@ -435,5 +466,3 @@ class DQNAgentOverOptions(DQNAgent):
                 q_values[node_index] = -np.inf
 
         return q_values
-
-

backends/learner_base.py

-from.policy_base import PolicyBase
+from .policy_base import PolicyBase
 import numpy as np
 
 
@@ -23,10 +23,10 @@ class LearnerBase(PolicyBase):
             setattr(self, prop, kwargs.get(prop, default))
 
     def train(self,
-            env,
-            nb_steps=50000,
-            visualize=False,
-            nb_max_episode_steps=200):
+              env,
+              nb_steps=50000,
+              visualize=False,
+              nb_max_episode_steps=200):
         """Train the learning agent on the environment.
 
         Args:

backends/manual_policy.py

 from .controller_base import ControllerBase
 
+
 class ManualPolicy(ControllerBase):
     """Manual policy execution using nodes and edges."""
 
-    def __init__(self, env, low_level_policies, transition_adj, start_node_alias):
+    def __init__(self, env, low_level_policies, transition_adj,
+                 start_node_alias):
         """Constructor for manual policy execution.
 
         Args:
@@ -13,7 +15,8 @@ class ManualPolicy(ControllerBase):
             start_node: starting node
         """
 
-        super(ManualPolicy, self).__init__(env, low_level_policies, start_node_alias)
+        super(ManualPolicy, self).__init__(env, low_level_policies,
+                                           start_node_alias)
         self.adj = transition_adj
 
     def _transition(self):
@@ -50,4 +53,4 @@ class ManualPolicy(ControllerBase):
 
         new_node = self._transition()
         if new_node is not None:
-            self.current_node = new_node
\ No newline at end of file
+            self.current_node = new_node

backends/mcts_learner.py

@@ -2,6 +2,7 @@ from .controller_base import ControllerBase
 import numpy as np
 import pickle
 
+
 class MCTSLearner(ControllerBase):
     """Monte Carlo Tree Search implementation using the UCB1 and
     progressive widening approach as explained in Paxton et al (2017).
@@ -9,8 +10,8 @@ class MCTSLearner(ControllerBase):
 
     _ucb_vals = set()
 
-    def __init__(self, env, low_level_policies,
-                 start_node_alias, max_depth=10):
+    def __init__(self, env, low_level_policies, start_node_alias,
+                 max_depth=10):
         """Constructor for MCTSLearner.
 
         Args:
@@ -22,10 +23,12 @@ class MCTSLearner(ControllerBase):
             max_depth: max depth of the MCTS tree; default 10 levels
         """
 
-        super(MCTSLearner, self).__init__(env, low_level_policies, start_node_alias)
+        super(MCTSLearner, self).__init__(env, low_level_policies,
+                                          start_node_alias)
 
         self.controller_args_defaults = {
-            "predictor": None  #P(s, o) learner class; forward pass should return the entire value from state s and option o
+            "predictor":
+            None  #P(s, o) learner class; forward pass should return the entire value from state s and option o
         }
         self.max_depth = max_depth
         #: store current node alias
@@ -48,11 +51,17 @@ class MCTSLearner(ControllerBase):
         # populate root node
         root_node_num, root_node_info = self._create_node(self.curr_node_alias)
         self.nodes[root_node_num] = root_node_info
-        self.adj[root_node_num] = set() # no children
+        self.adj[root_node_num] = set()  # no children
 
     def save_model(self, file_name="mcts.pickle"):
-        to_backup = {'N': self.N, 'M': self.M, 'TR': self.TR, 'nodes': self.nodes,
-            'adj': self.adj, 'new_node_num': self.new_node_num}
+        to_backup = {
+            'N': self.N,
+            'M': self.M,
+            'TR': self.TR,
+            'nodes': self.nodes,
+            'adj': self.adj,
+            'new_node_num': self.new_node_num
+        }
         with open(file_name, 'wb') as handle:
             pickle.dump(to_backup, handle, protocol=pickle.HIGHEST_PROTOCOL)
 
@@ -96,9 +105,9 @@ class MCTSLearner(ControllerBase):
         """
         dis_observation = ''
         for item in observation[12:20]:
-            if type(item)==bool:
+            if type(item) == bool:
                 dis_observation += '1' if item is True else '0'
-            if type(item)==int and item in [0, 1]:
+            if type(item) == int and item in [0, 1]:
                 dis_observation += str(item)
 
         env = self.current_node.env
@@ -163,7 +172,8 @@ class MCTSLearner(ControllerBase):
         dis_observation = self._to_discrete(observation)
         if (dis_observation, option) not in self.TR:
             self.TR[(dis_observation, option)] = 0
-        return self.TR[(dis_observation, option)] / (1+self._get_visitation_count(observation, option))
+        return self.TR[(dis_observation, option)] / (
+            1 + self._get_visitation_count(observation, option))
 
     def _get_possible_options(self):
         """Returns a set of options that can be taken from the current node.
@@ -172,7 +182,7 @@ class MCTSLearner(ControllerBase):
         """
 
         all_options = set(self.low_level_policies.keys())
-        
+
         # Filter nodes whose initiation condition are true
         filtered_options = set()
         for option_alias in all_options:
@@ -211,14 +221,15 @@ class MCTSLearner(ControllerBase):
 
         Returns Q values for next nodes
         """
-        
+
         Q = {}
-        Q1, Q2 = {}, {} # debug
+        Q1, Q2 = {}, {}  # debug
         dis_observation = self._to_discrete(observation)
         next_option_nums = self.adj[self.curr_node_num]
         for next_option_num in next_option_nums:
             next_option = self.nodes[next_option_num]["policy"]
-            Q1[next_option] = (self._get_q_star(observation, next_option)+200)/400
+            Q1[next_option] = (
+                self._get_q_star(observation, next_option) + 200) / 400
             Q[(dis_observation, next_option)] = \
                 Q1[next_option]
             Q2[next_option] = C * \
@@ -243,7 +254,7 @@ class MCTSLearner(ControllerBase):
         relevant_rewards = [value for key, value in self.TR.items() \
             if key[0] == dis_observation]
         sum_rewards = np.sum(relevant_rewards)
-        return sum_rewards / (1+self._get_visitation_count(observation))
+        return sum_rewards / (1 + self._get_visitation_count(observation))
 
     def _select(self, observation, depth=0, visualize=False):
         """MCTS selection function. For representation, we only use
@@ -266,7 +277,8 @@ class MCTSLearner(ControllerBase):
 
         if is_terminal or max_depth_reached:
             # print('MCTS went %d nodes deep' % depth)
-            return self._value(observation), 0 # TODO: replace with final goal reward
+            return self._value(
+                observation), 0  # TODO: replace with final goal reward
 
         Ns = self._get_visitation_count(observation)
         Nchildren = len(self.adj[self.curr_node_num])
@@ -288,19 +300,20 @@ class MCTSLearner(ControllerBase):
                 self.adj[self.curr_node_num].add(new_node_num)
 
         # Find o_star and do a transition, i.e. update curr_node
-        # Simulate / lookup; first change next 
-        next_observation, episode_R, o_star = self.do_transition(observation,
-            visualize=visualize)
+        # Simulate / lookup; first change next
+        next_observation, episode_R, o_star = self.do_transition(
+            observation, visualize=visualize)
 
         # Recursively select next node
-        remaining_v, all_ep_R = self._select(next_observation, depth+1, visualize=visualize)
+        remaining_v, all_ep_R = self._select(
+            next_observation, depth + 1, visualize=visualize)
 
         # Update values
         self.N[dis_observation] += 1
         self.M[(dis_observation, o_star)] += 1
         self.TR[(dis_observation, o_star)] += (episode_R + remaining_v)
 
-        return self._value(observation), all_ep_R+episode_R
+        return self._value(observation), all_ep_R + episode_R
 
     def traverse(self, observation, visualize=False):
         """Do a complete traversal from root to leaf. Assumes the
@@ -368,4 +381,4 @@ class MCTSLearner(ControllerBase):
             next_keys, next_values = list(Q1.keys()), list(Q1.values())
             o_star = next_keys[np.argmax(next_values)]
             print(Q1)
-        return o_star
\ No newline at end of file
+        return o_star

backends/online_mcts_controller.py

@@ -3,6 +3,7 @@ from .mcts_learner import MCTSLearner
 import tqdm
 import numpy as np
 
+
 class OnlineMCTSController(ControllerBase):
     """Online MCTS"""
 
@@ -13,12 +14,13 @@ class OnlineMCTSController(ControllerBase):
             env: env instance
             low_level_policies: low level policies dictionary
         """
-        super(OnlineMCTSController, self).__init__(env, low_level_policies, start_node_alias)
+        super(OnlineMCTSController, self).__init__(env, low_level_policies,
+                                                   start_node_alias)
         self.curr_node_alias = start_node_alias
         self.controller_args_defaults = {
             "predictor": None,
-            "max_depth": 5, # MCTS depth
-            "nb_traversals": 30, # MCTS traversals before decision
+            "max_depth": 5,  # MCTS depth
+            "nb_traversals": 30,  # MCTS traversals before decision
         }
 
     def set_current_node(self, node_alias):
@@ -48,12 +50,14 @@ class OnlineMCTSController(ControllerBase):
         env_before_mcts = orig_env.copy()
         self.change_low_level_references(env_before_mcts)
         print('Current Node: %s' % self.curr_node_alias)
-        mcts = MCTSLearner(self.env, self.low_level_policies, self.curr_node_alias)
+        mcts = MCTSLearner(self.env, self.low_level_policies,
+                           self.curr_node_alias)
         mcts.max_depth = self.max_depth
-        mcts.set_controller_args(predictor = self.predictor)
+        mcts.set_controller_args(predictor=self.predictor)
         # Do nb_traversals number of traversals, reset env to this point every time
         # print('Doing MCTS with params: max_depth = %d, nb_traversals = %d' % (self.max_depth, self.nb_traversals))
-        for num_epoch in range(self.nb_traversals): # tqdm.tqdm(range(self.nb_traversals)):
+        for num_epoch in range(
+                self.nb_traversals):  # tqdm.tqdm(range(self.nb_traversals)):
             mcts.curr_node_num = 0
             env_begin_epoch = env_before_mcts.copy()
             self.change_low_level_references(env_begin_epoch)
@@ -63,6 +67,7 @@ class OnlineMCTSController(ControllerBase):
         # Find the nodes from the root node
         mcts.curr_node_num = 0
         print('%s' % mcts._to_discrete(self.env.get_features_tuple()))
-        node_after_transition = mcts.get_best_node(self.env.get_features_tuple(), use_ucb=False)
+        node_after_transition = mcts.get_best_node(
+            self.env.get_features_tuple(), use_ucb=False)
         print('MCTS suggested next option: %s' % node_after_transition)
-        self.set_current_node(node_after_transition)
\ No newline at end of file
+        self.set_current_node(node_after_transition)

backends/policy_base.py

 class PolicyBase:
-	"""Abstract policy base from which every policy backend is defined
-	and inherited."""
\ No newline at end of file
+    """Abstract policy base from which every policy backend is defined
+	and inherited."""

backends/rl_controller.py

@@ -11,7 +11,8 @@ class RLController(ControllerBase):
             env: env instance
             low_level_policies: low level policies dictionary
         """
-        super(RLController, self).__init__(env, low_level_policies, start_node_alias)
+        super(RLController, self).__init__(env, low_level_policies,
+                                           start_node_alias)
         self.low_level_policy_aliases = list(self.low_level_policies.keys())
         self.trained_policy = None
         self.node_terminal_state_reached = False
@@ -32,6 +33,8 @@ class RLController(ControllerBase):
         if self.trained_policy is None:
             raise Exception(self.__class__.__name__ + \
                                  "trained_policy is not set. Use set_trained_policy().")
-        node_index_after_transition = self.trained_policy(self.env.get_features_tuple())
-        self.set_current_node(self.low_level_policy_aliases[node_index_after_transition])
-        self.node_terminal_state_reached = False
\ No newline at end of file
+        node_index_after_transition = self.trained_policy(
+            self.env.get_features_tuple())
+        self.set_current_node(
+            self.low_level_policy_aliases[node_index_after_transition])
+        self.node_terminal_state_reached = False

env/env_base.py

@@ -6,19 +6,22 @@ class GymCompliantEnvBase:
         """ Gym compliant step function which
             will be implemented in the subclass.
         """
-        raise NotImplemented(self.__class__.__name__ + "step is not implemented.")
+        raise NotImplemented(self.__class__.__name__ +
+                             "step is not implemented.")
 
     def reset(self):
         """ Gym compliant reset function which
             will be implemented in the subclass.
         """
-        raise NotImplemented(self.__class__.__name__ + "reset is not implemented.")
+        raise NotImplemented(self.__class__.__name__ +
+                             "reset is not implemented.")
 
     def render(self):
         """ Gym compliant step function which
             will be implemented in the subclass.
         """
-        raise NotImplemented(self.__class__.__name__ + "render is not implemented.")
+        raise NotImplemented(self.__class__.__name__ +
+                             "render is not implemented.")
 
 
 class EpisodicEnvBase(GymCompliantEnvBase):
@@ -77,13 +80,16 @@ class EpisodicEnvBase(GymCompliantEnvBase):
             return
 
         if self.terminal_reward_type == 'min':
-            self._r_terminal = r_obs if self._r_terminal is None else min(self._r_terminal, r_obs)
+            self._r_terminal = r_obs if self._r_terminal is None else min(
+                self._r_terminal, r_obs)
         elif self.terminal_reward_type == 'max':
-            self._r_terminal = r_obs if self._r_terminal is None else max(self._r_terminal, r_obs)
+            self._r_terminal = r_obs if self._r_terminal is None else max(
+                self._r_terminal, r_obs)
         elif self.terminal_reward_type == 'sum':
             self._r_terminal = r_obs if self._r_terminal is None else self._r_terminal + r_obs
         else:
-            raise AssertionError("The terminal_reward_type has to be 'min', 'max', or 'sum'")
+            raise AssertionError(
+                "The terminal_reward_type has to be 'min', 'max', or 'sum'")
 
     def step(self, u):
         # the penalty is a negative reward.
@@ -118,7 +124,8 @@ class EpisodicEnvBase(GymCompliantEnvBase):
             if LTL_precondition.enabled:
                 LTL_precondition.check_incremental(self.__mc_AP)
                 if LTL_precondition.result == Parser.FALSE:
-                    self._terminal_reward_superposition(EpisodicEnvBase._reward(LTL_precondition.penalty))
+                    self._terminal_reward_superposition(
+                        EpisodicEnvBase._reward(LTL_precondition.penalty))
                     violate = True
                     info['ltl_violation'] = LTL_precondition.str
                     # print("\nViolation of \"" + LTL_precondition.str + "\"")
@@ -177,7 +184,6 @@ class EpisodicEnvBase(GymCompliantEnvBase):
                 return True
         return False
 
-
     # TODO: replace these confusing methods reward and penalty, or not to use both reward and penalty for the property naming.
     @staticmethod
     def _reward(penalty):
@@ -186,4 +192,3 @@ class EpisodicEnvBase(GymCompliantEnvBase):
     @staticmethod
     def _penalty(reward):
         return None if reward is None else -reward
-

env/simple_intersection/__init__.py

-from .simple_intersection_env import SimpleIntersectionEnv
\ No newline at end of file
+from .simple_intersection_env import SimpleIntersectionEnv

env/simple_intersection/features.py

@@ -34,7 +34,7 @@ con_ego_feature_dict = {
     'pos_stop_region': 11
 }
 
-#: dis_ego_feature_dict contains all indexing information regarding 
+#: dis_ego_feature_dict contains all indexing information regarding
 #  each element of the ego vehicle's discrete feature vector.