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Apr 2, 2018
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Feature trainer ppo is continuous #520

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394678f
Corrected Wall Jump description (#501)
awjuliani Mar 18, 2018
9b42a11
[Refactor] renamed is_continuous to is_continuous_action and added is…
vincentpierre Mar 20, 2018
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2 changes: 1 addition & 1 deletion docs/Learning-Environment-Examples.md
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Original file line number Diff line number Diff line change
Expand Up @@ -109,7 +109,7 @@ If you would like to contribute environments, please see our
* -0.0005 for every step.
* +1.0 if the agent touches the goal.
* -1.0 if the agent falls off the platform.
* Brains: One brain with the following observation/action space.
* Brains: Two brains, each with the following observation/action space.
* Vector Observation space: (Continuous) 16 variables corresponding to position and velocities of agent, block, and goal, plus the height of the wall.
* Vector Action space: (Discrete) Size of 74, corresponding to 14 raycasts each detecting 4 possible objects. plus the global position of the agent and whether or not the agent is grounded.
* Visual Observations: None.
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15 changes: 8 additions & 7 deletions python/unitytrainers/ppo/trainer.py
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Expand Up @@ -75,7 +75,8 @@ def __init__(self, sess, env, brain_name, trainer_parameters, training, seed):
self.training_buffer = Buffer()
self.cumulative_rewards = {}
self.episode_steps = {}
self.is_continuous = (env.brains[brain_name].vector_action_space_type == "continuous")
self.is_continuous_action = (env.brains[brain_name].vector_action_space_type == "continuous")
self.is_continuous_observation = (env.brains[brain_name].vector_observation_space_type == "continuous")
self.use_observations = (env.brains[brain_name].number_visual_observations > 0)
self.use_states = (env.brains[brain_name].vector_observation_space_size > 0)
self.summary_path = trainer_parameters['summary_path']
Expand Down Expand Up @@ -169,7 +170,7 @@ def take_action(self, all_brain_info: AllBrainInfo):
feed_dict = {self.model.batch_size: len(curr_brain_info.vector_observations), self.model.sequence_length: 1}
run_list = [self.model.output, self.model.all_probs, self.model.value, self.model.entropy,
self.model.learning_rate]
if self.is_continuous:
if self.is_continuous_action:
run_list.append(self.model.epsilon)
elif self.use_recurrent:
feed_dict[self.model.prev_action] = np.reshape(curr_brain_info.previous_vector_actions, [-1])
Expand All @@ -183,7 +184,7 @@ def take_action(self, all_brain_info: AllBrainInfo):
curr_brain_info.memories = np.zeros((len(curr_brain_info.agents), self.m_size))
feed_dict[self.model.memory_in] = curr_brain_info.memories
run_list += [self.model.memory_out]
if (self.is_training and self.brain.vector_observation_space_type == "continuous" and
if (self.is_training and self.is_continuous_observation and
self.use_states and self.trainer_parameters['normalize']):
new_mean, new_variance = self.running_average(
curr_brain_info.vector_observations, steps, self.model.running_mean, self.model.running_variance)
Expand Down Expand Up @@ -240,7 +241,7 @@ def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainIn
if stored_info.memories.shape[1] == 0:
stored_info.memories = np.zeros((len(stored_info.agents), self.m_size))
self.training_buffer[agent_id]['memory'].append(stored_info.memories[idx])
if self.is_continuous:
if self.is_continuous_action:
epsi = stored_take_action_outputs[self.model.epsilon]
self.training_buffer[agent_id]['epsilons'].append(epsi[idx])
actions = stored_take_action_outputs[self.model.output]
Expand Down Expand Up @@ -286,7 +287,7 @@ def process_experiences(self, all_info: AllBrainInfo):
if info.memories.shape[1] == 0:
info.memories = np.zeros((len(info.vector_observations), self.m_size))
feed_dict[self.model.memory_in] = info.memories
if not self.is_continuous and self.use_recurrent:
if not self.is_continuous_action and self.use_recurrent:
feed_dict[self.model.prev_action] = np.reshape(info.previous_vector_actions, [-1])
value_next = self.sess.run(self.model.value, feed_dict)[l]
agent_id = info.agents[l]
Expand Down Expand Up @@ -358,7 +359,7 @@ def update_model(self):
self.model.advantage: np.array(_buffer['advantages'][start:end]).reshape([-1, 1]),
self.model.all_old_probs: np.array(
_buffer['action_probs'][start:end]).reshape([-1, self.brain.vector_action_space_size])}
if self.is_continuous:
if self.is_continuous_action:
feed_dict[self.model.epsilon] = np.array(
_buffer['epsilons'][start:end]).reshape([-1, self.brain.vector_action_space_size])
else:
Expand All @@ -368,7 +369,7 @@ def update_model(self):
feed_dict[self.model.prev_action] = np.array(
_buffer['prev_action'][start:end]).reshape([-1])
if self.use_states:
if self.brain.vector_observation_space_type == "continuous":
if self.is_continuous_observation:
feed_dict[self.model.vector_in] = np.array(
_buffer['states'][start:end]).reshape(
[-1, self.brain.vector_observation_space_size * self.brain.num_stacked_vector_observations])
Expand Down