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Multiple improvements and bug fixes (#66)

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* Multiple improvements and bug fixes:

    * Using lazy stacking to save on memory when using a replay buffer
    * Remove step counting for evaluation episodes
    * Reset game between heatup and training
    * Major bug fixes in NEC (is reproducing the paper results for pong now)
    * Image input rescaling to 0-1 is now optional
    * Change the terminal title to be the experiment name
    * Observation cropping for atari is now optional
    * Added random number of noop actions for gym to match the dqn paper
    * Fixed a bug where the evaluation episodes won't start with the max possible ale lives
    * Added a script for plotting the results of an experiment over all the atari games
This commit is contained in:
Itai Caspi
2018-02-26 12:29:07 +02:00
committed by GitHub
parent 4fe9cba445
commit a7206ed702
20 changed files with 465 additions and 158 deletions
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21
memories/memory.py
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@@ -80,9 +80,12 @@ class Episode(object):
total_return += current_discount * np.pad(rewards[i:], (0, i), 'constant', constant_values=0)
current_discount *= discount
# calculate the bootstrapped returns
bootstraps = np.array([np.squeeze(t.info['max_action_value']) for t in self.transitions[n_step_return:]])
bootstrapped_return = total_return + current_discount * np.pad(bootstraps, (0, n_step_return), 'constant',
constant_values=0)
if is_bootstrapped:
bootstraps = np.array([np.squeeze(t.info['action_value']) for t in self.transitions[n_step_return:]])
total_return += current_discount * np.pad(bootstraps, (0, n_step_return), 'constant', constant_values=0)
total_return = bootstrapped_return
for transition_idx in range(self.length()):
self.transitions[transition_idx].total_return = total_return[transition_idx]
@@ -114,7 +117,13 @@ class Episode(object):
return self.returns_table
def get_returns(self):
return [t.total_return for t in self.transitions]
return self.get_transitions_attribute('total_return')
def get_transitions_attribute(self, attribute_name):
if hasattr(self.transitions[0], attribute_name):
return [t.__dict__[attribute_name] for t in self.transitions]
else:
raise ValueError("The transitions have no such attribute name")
def to_batch(self):
batch = []
@@ -141,14 +150,12 @@ class Transition(object):
:param game_over: A boolean which should be True if the episode terminated after
the execution of the action.
"""
self.state = copy.deepcopy(state)
self.state['observation'] = np.array(self.state['observation'], copy=False)
self.state = state
self.action = action
self.reward = reward
self.total_return = None
if not next_state:
next_state = state
self.next_state = copy.deepcopy(next_state)
self.next_state['observation'] = np.array(self.next_state['observation'], copy=False)
self.next_state = next_state
self.game_over = game_over
self.info = {}