def __init__(self, model_config: core.ModelConfig, backend_name: str,
tf_eager_execution: bool):
"""
Args:
model_config: defines the model and environment to be used
backend_name: id of the backend to which this agent belongs to.
"""
global _tf_eager_execution_active
assert model_config is not None, "model_config not set."
assert backend_name
if _tf_eager_execution_active is None:
_tf_eager_execution_active = tf_eager_execution
assert _tf_eager_execution_active == tf_eager_execution, \
"Due to an incompatibility between tensorforce and tfagents their agents can not be instantiated in the" +\
"same python runtime instance (conflicting excpectations on tensorflows eager execution mode)."
self._backend_name: str = backend_name
self.model_config = model_config
self._agent_context: core.AgentContext = core.AgentContext(
self.model_config)
self._agent_context.gym._totals = monitor._register_gym_monitor(
self.model_config.original_env_name)
self.model_config.gym_env_name = self._agent_context.gym._totals.gym_env_name
self._preprocess_callbacks: List[core._PreProcessCallback] = [
plot._PreProcess()
]
self._callbacks: List[core.AgentCallback] = []
self._postprocess_callbacks: List[core._PostProcessCallback] = [
plot._PostProcess()
]
self._train_total_episodes_on_iteration_begin: int = 0
def _prepare_callbacks(self, callbacks: List[core.AgentCallback],
default_plots: Optional[bool],
default_plot_callbacks: List[plot._PlotCallback],
) -> List[core.AgentCallback]:
"""Adds the default callbacks and sorts all callbacks in the order
_PreProcessCallbacks, AgentCallbacks, _PostProcessCallbacks.
Args:
callbacks: existing callbacks to prepare
default_plots: if set or if None and callbacks does not contain plots then the default plots are added
default_plot_callbacks: plot callbacks to add.
"""
pre_process: List[core.AgentCallback] = [plot._PreProcess()]
agent: List[core.AgentCallback] = []
post_process: List[core.AgentCallback] = [plot._PostProcess()]
if default_plots is None:
default_plots = True
for c in callbacks:
default_plots = default_plots and (not isinstance(c, plot._PlotCallback))
if default_plots:
agent = default_plot_callbacks
for c in callbacks:
if isinstance(c, core._PreProcessCallback):
pre_process.append(c)
else:
if isinstance(c, core._PostProcessCallback):
post_process.append(c)
else:
agent.append(c)
result: List[core.AgentCallback] = pre_process + agent + post_process
return result
def __init__(self,
model_config: core.ModelConfig,
backend_name: str,
tensorflow_v2_eager: bool = True):
"""
Args:
model_config: defines the model and environment to be used
backend_name: id of the backend to which this agent belongs to.
tensorflow_v2_eager: the execution mode, enforced for this and all other backend agents.
"""
assert model_config is not None, "model_config not set."
assert backend_name
self._backend_name: str = backend_name
self._tensorflow_v2_eager = tensorflow_v2_eager
self.model_config = model_config
self._agent_context: core.AgentContext = core.AgentContext(
self.model_config)
self._agent_context.gym._totals = monitor._register_gym_monitor(
self.model_config.original_env_name)
self.model_config.gym_env_name = self._agent_context.gym._totals.gym_env_name
self._preprocess_callbacks: List[core._PreProcessCallback] = [
plot._PreProcess()
]
self._callbacks: List[core.AgentCallback] = []
self._postprocess_callbacks: List[core._PostProcessCallback] = [
plot._PostProcess()
]
self._train_total_episodes_on_iteration_begin: int = 0
self._initialize_tensorflow()