ray/rllib/examples/policy/random_policy.py

import numpy as np
import random

from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import override


class RandomPolicy(Policy):
    """Hand-coded policy that returns random actions."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    @override(Policy)
    def compute_actions(self,
                        obs_batch,
                        state_batches=None,
                        prev_action_batch=None,
                        prev_reward_batch=None,
                        **kwargs):
        # Alternatively, a numpy array would work here as well.
        # e.g.: np.array([random.choice([0, 1])] * len(obs_batch))
        return [self.action_space.sample() for _ in obs_batch], [], {}

    @override(Policy)
    def learn_on_batch(self, samples):
        """No learning."""
        return {}

    @override(Policy)
    def compute_log_likelihoods(self,
                                actions,
                                obs_batch,
                                state_batches=None,
                                prev_action_batch=None,
                                prev_reward_batch=None):
        return np.array([random.random()] * len(obs_batch))