def train_batch(self, batch_info: BatchInfo):
batch_info['<STR_LIT>'] = []<EOL>self.on_policy_train_batch(batch_info)<EOL>if self.settings.experience_replay > <NUM_LIT:0> and self.env_roller.is_ready_for_sampling():<EOL><INDENT>if self.settings.stochastic_experience_replay:<EOL><INDENT>experience_replay_count = np.random.poisson(self.settings.experience_replay)<EOL><DEDENT>else:<EOL><INDENT>experience_replay_count = self.settings.experience_replay<EOL><DEDENT>for i in range(experience_replay_count):<EOL><INDENT>self.off_policy_train_batch(batch_info)<EOL><DEDENT><DEDENT>batch_info.aggregate_key('<STR_LIT>')<EOL>
Single, most atomic 'step' of learning this reinforcer can perform