from gym.spaces import Space from typing import Optional from ray.rllib.utils.exploration.epsilon_greedy import EpsilonGreedy from ray.rllib.utils.schedules import ConstantSchedule class PerWorkerEpsilonGreedy(EpsilonGreedy): """A per-worker epsilon-greedy class for distributed algorithms. Sets the epsilon schedules of individual workers to a constant: 0.4 ^ (1 + [worker-index] / float([num-workers] - 1) * 7) See Ape-X paper. """ def __init__( self, action_space: Space, *, framework: str, num_workers: Optional[int], worker_index: Optional[int], **kwargs ): """Create a PerWorkerEpsilonGreedy exploration class. Args: action_space: The gym action space used by the environment. num_workers: The overall number of workers used. worker_index: The index of the Worker using this Exploration. framework: One of None, "tf", "torch". """ epsilon_schedule = None # Use a fixed, different epsilon per worker. See: Ape-X paper. assert worker_index <= num_workers, (worker_index, num_workers) if num_workers > 0: if worker_index > 0: # From page 5 of https://arxiv.org/pdf/1803.00933.pdf alpha, eps, i = 7, 0.4, worker_index - 1 num_workers_minus_1 = float(num_workers - 1) if num_workers > 1 else 1.0 constant_eps = eps ** (1 + (i / num_workers_minus_1) * alpha) epsilon_schedule = ConstantSchedule(constant_eps, framework=framework) # Local worker should have zero exploration so that eval # rollouts run properly. else: epsilon_schedule = ConstantSchedule(0.0, framework=framework) super().__init__( action_space, epsilon_schedule=epsilon_schedule, framework=framework, num_workers=num_workers, worker_index=worker_index, **kwargs )