def _generate_reset_input(self, training, config) -> UnityRLInput:
rl_in = UnityRLInput()
rl_in.is_training = training
rl_in.environment_parameters.CopyFrom(EnvironmentParametersProto())
for key in config:
rl_in.environment_parameters.float_parameters[key] = config[key]
rl_in.command = 1
return self.wrap_unity_input(rl_in)
def _generate_reset_input(self, training, config) -> UnityRLInput:
rl_in = UnityRLInput()
rl_in.is_training = training
rl_in.environment_parameters.CopyFrom(EnvironmentParametersProto())
for key in config:
rl_in.environment_parameters.float_parameters[key] = config[key]
rl_in.command = 1
return self.wrap_unity_input(rl_in)
def _generate_step_input(self, vector_action, memory,
text_action) -> UnityRLInput:
rl_in = UnityRLInput()
for b in vector_action:
n_agents = self._n_agents[b]
if n_agents == 0:
continue
_a_s = len(vector_action[b]) // n_agents
_m_s = len(memory[b]) // n_agents
for i in range(n_agents):
action = AgentActionProto(
vector_actions=vector_action[b][i * _a_s:(i + 1) * _a_s],
memories=memory[b][i * _m_s:(i + 1) * _m_s],
text_actions=text_action[b][i])
rl_in.agent_actions[b].value.extend([action])
rl_in.command = 0
return self.wrap_unity_input(rl_in)
def _generate_step_input(self, vector_action, memory, text_action) -> UnityRLInput:
rl_in = UnityRLInput()
for b in vector_action:
n_agents = self._n_agents[b]
if n_agents == 0:
continue
_a_s = len(vector_action[b]) // n_agents
_m_s = len(memory[b]) // n_agents
for i in range(n_agents):
action = AgentActionProto(
vector_actions=vector_action[b][i*_a_s: (i+1)*_a_s],
memories=memory[b][i*_m_s: (i+1)*_m_s],
text_actions=text_action[b][i]
)
rl_in.agent_actions[b].value.extend([action])
rl_in.command = 0
return self.wrap_unity_input(rl_in)
def _generate_record_input(self) -> UnityRLInput:
rl_in = UnityRLInput()
rl_in.command = UnityCommand.RECORD
return self.wrap_unity_input(rl_in)