ray/rllib/utils/exploration/epsilon_greedy.py

import gym
import numpy as np
import tree  # pip install dm_tree
import random
from typing import Union, Optional

from ray.rllib.models.torch.torch_action_dist import TorchMultiActionDistribution
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.utils.annotations import override
from ray.rllib.utils.exploration.exploration import Exploration, TensorType
from ray.rllib.utils.framework import try_import_tf, try_import_torch, get_variable
from ray.rllib.utils.from_config import from_config
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.schedules import Schedule, PiecewiseSchedule
from ray.rllib.utils.torch_utils import FLOAT_MIN

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()


class EpsilonGreedy(Exploration):
    """Epsilon-greedy Exploration class that produces exploration actions.

    When given a Model's output and a current epsilon value (based on some
    Schedule), it produces a random action (if rand(1) < eps) or
    uses the model-computed one (if rand(1) >= eps).
    """

    def __init__(
        self,
        action_space: gym.spaces.Space,
        *,
        framework: str,
        initial_epsilon: float = 1.0,
        final_epsilon: float = 0.05,
        warmup_timesteps: int = 0,
        epsilon_timesteps: int = int(1e5),
        epsilon_schedule: Optional[Schedule] = None,
        **kwargs,
    ):
        """Create an EpsilonGreedy exploration class.

        Args:
            action_space: The action space the exploration should occur in.
            framework: The framework specifier.
            initial_epsilon: The initial epsilon value to use.
            final_epsilon: The final epsilon value to use.
            warmup_timesteps: The timesteps over which to not change epsilon in the
                beginning.
            epsilon_timesteps: The timesteps (additional to `warmup_timesteps`)
                after which epsilon should always be `final_epsilon`.
                E.g.: warmup_timesteps=20k epsilon_timesteps=50k -> After 70k timesteps,
                epsilon will reach its final value.
            epsilon_schedule: An optional Schedule object
                to use (instead of constructing one from the given parameters).
        """
        assert framework is not None
        super().__init__(action_space=action_space, framework=framework, **kwargs)

        self.epsilon_schedule = from_config(
            Schedule, epsilon_schedule, framework=framework
        ) or PiecewiseSchedule(
            endpoints=[
                (0, initial_epsilon),
                (warmup_timesteps, initial_epsilon),
                (warmup_timesteps + epsilon_timesteps, final_epsilon),
            ],
            outside_value=final_epsilon,
            framework=self.framework,
        )

        # The current timestep value (tf-var or python int).
        self.last_timestep = get_variable(
            np.array(0, np.int64),
            framework=framework,
            tf_name="timestep",
            dtype=np.int64,
        )

        # Build the tf-info-op.
        if self.framework == "tf":
            self._tf_state_op = self.get_state()

    @override(Exploration)
    def get_exploration_action(
        self,
        *,
        action_distribution: ActionDistribution,
        timestep: Union[int, TensorType],
        explore: Optional[Union[bool, TensorType]] = True,
    ):

        if self.framework in ["tf2", "tf", "tfe"]:
            return self._get_tf_exploration_action_op(
                action_distribution, explore, timestep
            )
        else:
            return self._get_torch_exploration_action(
                action_distribution, explore, timestep
            )

    def _get_tf_exploration_action_op(
        self,
        action_distribution: ActionDistribution,
        explore: Union[bool, TensorType],
        timestep: Union[int, TensorType],
    ) -> "tf.Tensor":
        """TF method to produce the tf op for an epsilon exploration action.

        Args:
            action_distribution: The instantiated ActionDistribution object
                to work with when creating exploration actions.

        Returns:
            The tf exploration-action op.
        """
        # TODO: Support MultiActionDistr for tf.
        q_values = action_distribution.inputs
        epsilon = self.epsilon_schedule(
            timestep if timestep is not None else self.last_timestep
        )

        # Get the exploit action as the one with the highest logit value.
        exploit_action = tf.argmax(q_values, axis=1)

        batch_size = tf.shape(q_values)[0]
        # Mask out actions with q-value=-inf so that we don't even consider
        # them for exploration.
        random_valid_action_logits = tf.where(
            tf.equal(q_values, tf.float32.min),
            tf.ones_like(q_values) * tf.float32.min,
            tf.ones_like(q_values),
        )
        random_actions = tf.squeeze(
            tf.random.categorical(random_valid_action_logits, 1), axis=1
        )

        chose_random = (
            tf.random.uniform(
                tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32
            )
            < epsilon
        )

        action = tf.cond(
            pred=tf.constant(explore, dtype=tf.bool)
            if isinstance(explore, bool)
            else explore,
            true_fn=(lambda: tf.where(chose_random, random_actions, exploit_action)),
            false_fn=lambda: exploit_action,
        )

        if self.framework in ["tf2", "tfe"] and not self.policy_config["eager_tracing"]:
            self.last_timestep = timestep
            return action, tf.zeros_like(action, dtype=tf.float32)
        else:
            assign_op = tf1.assign(self.last_timestep, tf.cast(timestep, tf.int64))
            with tf1.control_dependencies([assign_op]):
                return action, tf.zeros_like(action, dtype=tf.float32)

    def _get_torch_exploration_action(
        self,
        action_distribution: ActionDistribution,
        explore: bool,
        timestep: Union[int, TensorType],
    ) -> "torch.Tensor":
        """Torch method to produce an epsilon exploration action.

        Args:
            action_distribution (ActionDistribution): The instantiated
                ActionDistribution object to work with when creating
                exploration actions.

        Returns:
            The exploration-action.
        """
        q_values = action_distribution.inputs
        self.last_timestep = timestep
        exploit_action = action_distribution.deterministic_sample()
        batch_size = q_values.size()[0]
        action_logp = torch.zeros(batch_size, dtype=torch.float)

        # Explore.
        if explore:
            # Get the current epsilon.
            epsilon = self.epsilon_schedule(self.last_timestep)
            if isinstance(action_distribution, TorchMultiActionDistribution):
                exploit_action = tree.flatten(exploit_action)
                for i in range(batch_size):
                    if random.random() < epsilon:
                        # TODO: (bcahlit) Mask out actions
                        random_action = tree.flatten(self.action_space.sample())
                        for j in range(len(exploit_action)):
                            exploit_action[j][i] = torch.tensor(random_action[j])
                exploit_action = tree.unflatten_as(
                    action_distribution.action_space_struct, exploit_action
                )

                return exploit_action, action_logp

            else:
                # Mask out actions, whose Q-values are -inf, so that we don't
                # even consider them for exploration.
                random_valid_action_logits = torch.where(
                    q_values <= FLOAT_MIN,
                    torch.ones_like(q_values) * 0.0,
                    torch.ones_like(q_values),
                )
                # A random action.
                random_actions = torch.squeeze(
                    torch.multinomial(random_valid_action_logits, 1), axis=1
                )

                # Pick either random or greedy.
                action = torch.where(
                    torch.empty((batch_size,)).uniform_().to(self.device) < epsilon,
                    random_actions,
                    exploit_action,
                )

                return action, action_logp
        # Return the deterministic "sample" (argmax) over the logits.
        else:
            return exploit_action, action_logp

    @override(Exploration)
    def get_state(self, sess: Optional["tf.Session"] = None):
        if sess:
            return sess.run(self._tf_state_op)
        eps = self.epsilon_schedule(self.last_timestep)
        return {
            "cur_epsilon": convert_to_numpy(eps) if self.framework != "tf" else eps,
            "last_timestep": convert_to_numpy(self.last_timestep)
            if self.framework != "tf"
            else self.last_timestep,
        }

    @override(Exploration)
    def set_state(self, state: dict, sess: Optional["tf.Session"] = None) -> None:
        if self.framework == "tf":
            self.last_timestep.load(state["last_timestep"], session=sess)
        elif isinstance(self.last_timestep, int):
            self.last_timestep = state["last_timestep"]
        else:
            self.last_timestep.assign(state["last_timestep"])