from gym.spaces import Box
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)

        # Whether for compute_actions, the bounds given in action_space
        # should be ignored (default: False). This is to test action-clipping
        # and any Env's reaction to bounds breaches.
        if self.config.get("ignore_action_bounds", False) and \
                isinstance(self.action_space, Box):
            self.action_space_for_sampling = Box(
                -float("inf"),
                float("inf"),
                shape=self.action_space.shape,
                dtype=self.action_space.dtype)
        else:
            self.action_space_for_sampling = self.action_space

    @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_for_sampling.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))
