@store_args<EOL><INDENT>def __init__(self, venv, policy, dims, logger, T, rollout_batch_size=<NUM_LIT:1>,<EOL>exploit=False, use_target_net=False, compute_Q=False, noise_eps=<NUM_LIT:0>,<EOL>random_eps=<NUM_LIT:0>, history_len=<NUM_LIT:100>, render=False, monitor=False, **kwargs):<DEDENT>
assert self.T > <NUM_LIT:0><EOL>self.info_keys = [key.replace('<STR_LIT>', '<STR_LIT>') for key in dims.keys() if key.startswith('<STR_LIT>')]<EOL>self.success_history = deque(maxlen=history_len)<EOL>self.Q_history = deque(maxlen=history_len)<EOL>self.n_episodes = <NUM_LIT:0><EOL>self.reset_all_rollouts()<EOL>self.clear_history()<EOL>
Rollout worker generates experience by interacting with one or many environments.

        Args:
            make_env (function): a factory function that creates a new instance of the environment
                when called
            policy (object): the policy that is used to act
            dims (dict of ints): the dimensions for observations (o), goals (g), and actions (u)
            logger (object): the logger that is used by the rollout worker
            rollout_batch_size (int): the number of parallel rollouts that should be used
            exploit (boolean): whether or not to exploit, i.e. to act optimally according to the
                current policy without any exploration
            use_target_net (boolean): whether or not to use the target net for rollouts
            compute_Q (boolean): whether or not to compute the Q values alongside the actions
            noise_eps (float): scale of the additive Gaussian noise
            random_eps (float): probability of selecting a completely random action
            history_len (int): length of history for statistics smoothing
            render (boolean): whether or not to render the rollouts