def __init__(self,
environment,
function_approximator,
config=None,
summary=None):
self.config = config or Config()
assert isinstance(config, Config)
"""
Parameters in config:
Name: Type: Default: Description: (Omitted when self-explanatory)
store_summary bool False store the summary of the agent (return per episode)
"""
self.store_summary = check_attribute_else_default(
self.config, 'store_summary', False)
if self.store_summary:
assert isinstance(summary, dict)
self.summary = summary
check_dict_else_default(self.summary, 'return_per_episode', [])
" Other Parameters "
# Function Approximator: used to approximate the Q-Values
self.fa = function_approximator
# Environment that the agent is interacting with
self.env = environment
# Summaries
self.cumulative_reward = 0
def __init__(self, config=None, summary=None):
assert isinstance(config, Config)
""" Parameters:
Name: Type Default: Description(omitted when self-explanatory):
max_actions int 1000 The max number of actions executed before forcing
a time out
save_summary bool False Whether to save a summary of the environment
"""
self.max_actions = check_attribute_else_default(config,
'max_actions',
default_value=1000)
self.save_summary = check_attribute_else_default(config,
'save_summary',
default_value=False)
self.summary = summary
if self.save_summary:
assert isinstance(self.summary, dict)
check_dict_else_default(self.summary, "steps_per_episode", [])
" Inner state of the environment "
self.step_count = 0
self.current_state = self.reset()
self.actions = np.array(
[0, 1, 2], dtype=int) # 0 = backward, 1 = coast, 2 = forward
self.high = np.array([0.5, 0.07], dtype=np.float32)
self.low = np.array([-1.2, -0.07], dtype=np.float32)
self.action_dictionary = {
0: -1, # accelerate backwards
1: 0, # coast
2: 1
} # accelerate forwards
def __init__(self, config, summary=None):
assert isinstance(config, Config)
""" Parameters:
Name: Type Default: Description(omitted when self-explanatory):
# environment parameters
max_episode_length int 500000 The max number of actions executed before forcing
a time out
norm_state bool True Normalize the state to [-1,1]
# summary parameters
store_summary bool False Whether to store the summary of the environment
number_of_steps int 500000 Total number of environment steps
"""
check_attribute(config, 'current_step', 0)
self.config = config
# environment related variables
self.max_episode_length = check_attribute(config,
'max_episode_length',
default_value=500000)
self.norm_state = check_attribute(config,
'norm_state',
default_value=True)
# summary related variables
self.store_summary = check_attribute(config,
'store_summary',
default_value=False)
self.number_of_steps = check_attribute(config,
'number_of_steps',
default_value=500000)
self.summary = summary
if self.store_summary:
assert isinstance(self.summary, dict)
self.reward_per_step = np.zeros(self.number_of_steps,
dtype=np.float64)
check_dict_else_default(self.summary, "steps_per_episode", [])
check_dict_else_default(self.summary, "reward_per_step",
self.reward_per_step)
# internal state of the environment
self.episode_step_count = 0
position = -0.6 + np.random.random() * 0.2
velocity = 0.0
self.current_state = np.array((position, velocity), dtype=np.float64)
self.actions = np.array(
[0, 1, 2], dtype=int) # 0 = backward, 1 = coast, 2 = forward
self.high = np.array([0.5, 0.07], dtype=np.float64)
self.low = np.array([-1.2, -0.07], dtype=np.float64)
self.action_dictionary = {
0: -1, # accelerate backwards
1: 0, # coast
2: 1
} # accelerate forwards
def __init__(self, config, summary=None):
""" Parameters:
Name: Type Default: Description(omitted when self-explanatory):
max_episode_length int 200000 The max number of steps executed in an episoe
before forcing a time out
norm_state bool True Normalize the state to [-1,1]
store_summary bool False Whether to store the summary of the environment
number_of_steps int 200000 Total number of environment steps
"""
check_attribute(config, 'current_step', 0)
self.config = config
# environment parameters
self.max_episode_length = check_attribute(config, 'max_episode_length',
200000)
self.norm_state = check_attribute(config, 'norm_state', True)
# summary parameters
self.store_summary = check_attribute(config,
'store_summary',
default_value=False)
self.summary = summary
self.number_of_steps = check_attribute(config, 'number_of_steps',
200000)
if self.store_summary:
assert isinstance(self.summary, dict)
self.reward_per_step = np.zeros(self.number_of_steps,
dtype=np.float64)
check_dict_else_default(self.summary, "steps_per_episode", [])
check_dict_else_default(self.summary, "reward_per_step",
self.reward_per_step)
self.num_action = 4
self.num_state = 2
""" Inner state of the environment """
self.episode_step_count = 0
self.state = np.float64(
np.random.uniform(low=0.0, high=0.1, size=(2, )))
self.puddle1 = Puddle(0.45, 0.75, 0.10, 0.75, 0.1, 0.35)
self.puddle2 = Puddle(0.45, 0.80, 0.45, 0.40, 0.1, 0.4)
self.pworld_min_x = 0.0
self.pworld_max_x = 1.0
self.pworld_min_y = 0.0
self.pworld_max_y = 1.0
self.goalDimension = 0.05
self.defDisplacement = 0.05
self.goalXCoor = self.pworld_max_x - self.goalDimension
self.goalYCoor = self.pworld_max_y - self.goalDimension
def __init__(self,
environment,
function_approximator,
behaviour_policy,
er_buffer,
config=None,
summary=None,
reshape=True):
"""
Summary Name: return_per_episode
"""
self.config = config or Config()
assert isinstance(config, Config)
"""
Parameters in config:
Name: Type: Default: Description: (Omitted when self-explanatory)
save_summary bool False save the summary of the agent (return per episode)
er_start_size int 0 number of steps sampled before training starts
er_init_steps_count int 0 number of initial steps taken so far
fixed_tpolicy bool False whether the policy is fixed (e.g., a function of
the state) or changes over time
(e.g., epsilon-greedy or a function of the q-values)
"""
self.save_summary = check_attribute_else_default(
self.config, 'save_summary', False)
self.er_start_size = check_attribute_else_default(
self.config, 'er_start_size', 0)
check_attribute_else_default(self.config, 'er_init_steps_count', 0)
self.fixed_tpolicy = check_attribute_else_default(
self.config, 'fixed_tpolicy', False)
if self.save_summary:
assert isinstance(summary, dict)
self.summary = summary
check_dict_else_default(self.summary, 'return_per_episode', [])
" Other Parameters "
# Behaviour
self.bpolicy = behaviour_policy
# Experience Replay Buffer
self.er_buffer = er_buffer
# Function Approximator: used to approximate the Q-Values
self.fa = function_approximator
# Environment that the agent is interacting with
self.env = environment
# Summaries
self.cumulative_reward = 0
# Whether to reshape the mountain car observations
self.reshape = reshape
def __init__(self, config=None, summary=None):
assert isinstance(config, Config)
""" Parameters:
Name: Type Default: Description(omitted when self-explanatory):
max_actions int 1000 The max number of actions executed before forcing
a time out
save_summary bool False Whether to save a summary of the environment
"""
self.max_actions = check_attribute_else_default(config, 'max_actions', default_value=500)
self.save_summary = check_attribute_else_default(config, 'save_summary', default_value=False)
self.summary = summary
if self.save_summary:
assert isinstance(self.summary, dict)
check_dict_else_default(self.summary, "steps_per_episode", [])
" Inner state of the environment "
self.step_count = 0
self.openai_env = gym.make('Acrobot-v1')
self.actions = np.array([0, 1, 2], dtype=np.int8)
self.high = np.array([np.pi * 2, np.pi * 2, 12.56637096, 28.27433395], np.float64)
self.low = np.array([0.0, 0.0, -12.56637096, -28.27433395], dtype=np.float64)
self.current_state = self.reset()
def __init__(self, config, summary=None):
""" Parameters:
Name: Type Default: Description(omitted when self-explanatory):
max_actions int 1000 The max number of actions executed before forcing
a time out
norm_state bool True Normalize the state to [-1,1]
store_summary bool False Whether to store the summary of the environment
"""
self.norm_state = check_attribute_else_default(config, 'norm_state', True)
self.max_actions = check_attribute_else_default(config, 'max_actions', 1000)
self.store_summary = check_attribute_else_default(config, 'store_summary', False)
self.summary = summary
if self.store_summary:
assert isinstance(self.summary, dict)
check_dict_else_default(self.summary, "steps_per_episode", [])
self.num_actions = 3
self.state_dims = 4
" Inner state of the environment "
self.step_count = 0
self.current_state = np.float64(np.random.uniform(low=-0.5, high=0.5, size=(4,)))
self.MAX_VEL_1 = 4 * np.pi
self.MAX_VEL_2 = 9 * np.pi
self.MAX_THETA_1 = np.pi
self.MAX_THETA_2 = np.pi
self.m1 = 1.0
self.m2 = 1.0
self.l1 = 1.0
self.l2 = 1.0
self.lc1 = 0.5
self.lc2 = 0.5
self.I1 = 1.0
self.I2 = 1.0
self.g = 9.8
self.dt = 0.05
self.acrobotGoalPosition = 1.0
def __init__(self, config, summary=None):
assert isinstance(config, Config)
""" Parameters:
Name: Type Default: Description(omitted when self-explanatory):
max_episode_length int 500000 The max number of steps executed in an episoe
before forcing a time out
norm_state bool True Normalize the state to [-1,1]
display bool False Whether to display the screen of the game
init_lives int 3 Number of lives at the start of the game
store_summary bool False Whether to store the summary of the environment
number_of_steps int 500000 Total number of environment steps
"""
check_attribute(config, 'current_step', 0)
self.config = config
# environment parameters
self.max_episode_length = check_attribute(config,
'max_episode_length',
default_value=500000)
self.norm_state = check_attribute(config,
'norm_state',
default_value=True)
self.display = False
self.init_lives = 3
# self.display = check_attribute(config, 'display', default_value=False)
# self.init_lives = check_attribute(config, 'init_lives', default_value=3)
# summary parameters
self.store_summary = check_attribute(config,
'store_summary',
default_value=False)
self.summary = summary
self.number_of_steps = check_attribute(config, 'number_of_steps',
500000)
if self.store_summary:
assert isinstance(self.summary, dict)
self.reward_per_step = np.zeros(self.number_of_steps,
dtype=np.float64)
check_dict_else_default(self.summary, "steps_per_episode", [])
check_dict_else_default(self.summary, "reward_per_step",
self.reward_per_step)
# setting up original catcher environment with the specified parameters
self.catcherOb = Catcher(init_lives=self.init_lives)
if not self.display:
# do not open a pygame window
os.putenv('SDL_VIDEODRIVER', 'fbcon')
os.environ["SDL_VIDEODRIVER"] = "dummy"
if self.norm_state:
self.pOb = PLE(self.catcherOb,
fps=30,
state_preprocessor=get_ob_normalize,
display_screen=self.display)
else:
self.pOb = PLE(self.catcherOb,
fps=30,
state_preprocessor=get_ob,
display_screen=self.display)
self.pOb.init()
# environment internal state
self.actions = [
97, None, 100
] # self.pOb.getActionSet() (left = 97, do nothing = None, right = 100)
self.num_action = 3
self.num_state = 4
self.episode_step_count = 0
self.pOb.reset_game()
self.current_state = self.pOb.getGameState()
def __init__(self,
optimizer,
target_network,
update_network,
er_buffer,
config=None,
tf_session=None,
summary=None):
"""
Summary Names:
cumulative_loss
training_steps
"""
assert isinstance(config, Config)
self.config = config
"""
Parameters in config:
Name: Type: Default: Description: (Omitted when self-explanatory)
alpha float 0.00025 step size parameter
obs_dims list [4,84,84] the dimensions of the obsevations
tnetwork_update_freq int 10,000 number of updates before updating the target network
update_count int 0 number of updates performed
save_summary bool False indicates whether to save a summary of training
"""
self.alpha = check_attribute_else_default(self.config, 'alpha',
0.00025)
self.obs_dims = check_attribute_else_default(self.config, 'obs_dims',
[4, 84, 84])
self.tnetwork_update_freq = check_attribute_else_default(
self.config, 'tnetwork_update_freq', 10000)
self.save_summary = check_attribute_else_default(
self.config, 'save_summary', False)
check_attribute_else_default(self.config, 'update_count', 0)
self.summary = summary
if self.save_summary:
assert isinstance(self.summary, dict)
check_dict_else_default(self.summary, 'cumulative_loss', [])
check_dict_else_default(self.summary, 'training_steps', [])
self.training_steps = 0
self.cumulative_loss = 0
""" Other Parameters """
" Experience Replay Buffer and Return Function "
self.er_buffer = er_buffer
" Neural Network Models "
self.target_network = target_network # Target Network
self.update_network = update_network # Update Network
" Training and Learning Evaluation: Tensorflow and variables initializer "
self.optimizer = optimizer(self.alpha)
self.sess = tf_session or tf.Session()
" Train step "
self.train_step = self.optimizer.minimize(
self.update_network.train_loss,
var_list=self.update_network.train_vars[0])
" Initializing variables in the graph"
for var in tf.global_variables():
self.sess.run(var.initializer)
" Copy Weights to Target Network Operator "
unetwork_vars = tf.get_collection(self.update_network.name)
tnetwork_vars = tf.get_collection(self.target_network.name)
copy_ops = [
target_var.assign(update_var)
for target_var, update_var in zip(tnetwork_vars, unetwork_vars)
]
self.copy_to_target = tf.group(*copy_ops)
self.sess.run(self.copy_to_target)