ml-agents/ml-agents/mlagents/trainers/tests/simple_test_envs.py

import random
from typing import Dict, List, Any, Tuple
import numpy as np

from mlagents_envs.base_env import (
    BaseEnv,
    BehaviorSpec,
    HybridBehaviorSpec,
    DecisionSteps,
    TerminalSteps,
    ActionType,
    BehaviorMapping,
    BehaviorName,
    HybridAction,
)
from mlagents_envs.tests.test_rpc_utils import proto_from_steps_and_action
from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
    AgentInfoActionPairProto,
)

OBS_SIZE = 1
VIS_OBS_SIZE = (20, 20, 3)
STEP_SIZE = 0.1

TIME_PENALTY = 0.01
MIN_STEPS = int(1.0 / STEP_SIZE) + 1
SUCCESS_REWARD = 1.0 + MIN_STEPS * TIME_PENALTY


def clamp(x, min_val, max_val):
    return max(min_val, min(x, max_val))


class SimpleEnvironment(BaseEnv):
    """
    Very simple "game" - the agent has a position on [-1, 1], gets a reward of 1 if it reaches 1, and a reward of -1 if
    it reaches -1. The position is incremented by the action amount (clamped to [-step_size, step_size]).
    """

    def __init__(
        self,
        brain_names,
        use_discrete,
        step_size=STEP_SIZE,
        num_visual=0,
        num_vector=1,
        vis_obs_size=VIS_OBS_SIZE,
        vec_obs_size=OBS_SIZE,
        action_size=1,
    ):
        super().__init__()
        self.discrete = use_discrete
        self.num_visual = num_visual
        self.num_vector = num_vector
        self.vis_obs_size = vis_obs_size
        self.vec_obs_size = vec_obs_size
        action_type = ActionType.DISCRETE if use_discrete else ActionType.CONTINUOUS
        self.behavior_spec = BehaviorSpec(
            self._make_obs_spec(),
            action_type,
            tuple(2 for _ in range(action_size)) if use_discrete else action_size,
        )
        self.action_size = action_size
        self.names = brain_names
        self.positions: Dict[str, List[float]] = {}
        self.step_count: Dict[str, float] = {}
        self.random = random.Random(str(self.behavior_spec))
        self.goal: Dict[str, int] = {}
        self.action = {}
        self.rewards: Dict[str, float] = {}
        self.final_rewards: Dict[str, List[float]] = {}
        self.step_result: Dict[str, Tuple[DecisionSteps, TerminalSteps]] = {}
        self.agent_id: Dict[str, int] = {}
        self.step_size = step_size  # defines the difficulty of the test

        for name in self.names:
            self.agent_id[name] = 0
            self.goal[name] = self.random.choice([-1, 1])
            self.rewards[name] = 0
            self.final_rewards[name] = []
            self._reset_agent(name)
            self.action[name] = None
            self.step_result[name] = None

    def _make_obs_spec(self) -> List[Any]:
        obs_spec: List[Any] = []
        for _ in range(self.num_vector):
            obs_spec.append((self.vec_obs_size,))
        for _ in range(self.num_visual):
            obs_spec.append(self.vis_obs_size)
        return obs_spec

    def _make_obs(self, value: float) -> List[np.ndarray]:
        obs = []
        for _ in range(self.num_vector):
            obs.append(np.ones((1, self.vec_obs_size), dtype=np.float32) * value)
        for _ in range(self.num_visual):
            obs.append(np.ones((1,) + self.vis_obs_size, dtype=np.float32) * value)
        return obs

    @property
    def behavior_specs(self):
        behavior_dict = {}
        for n in self.names:
            behavior_dict[n] = self.behavior_spec
        return BehaviorMapping(behavior_dict)

    def set_action_for_agent(self, behavior_name, agent_id, action):
        pass

    def set_actions(self, behavior_name, action):
        self.action[behavior_name] = action

    def get_steps(self, behavior_name):
        return self.step_result[behavior_name]

    def _take_action(self, name: str) -> bool:
        deltas = []
        for _act in self.action[name][0]:
            if self.discrete:
                deltas.append(1 if _act else -1)
            else:
                deltas.append(_act)
        for i, _delta in enumerate(deltas):
            _delta = clamp(_delta, -self.step_size, self.step_size)
            self.positions[name][i] += _delta
            self.positions[name][i] = clamp(self.positions[name][i], -1, 1)
            self.step_count[name] += 1
            # Both must be in 1.0 to be done
        done = all(pos >= 1.0 or pos <= -1.0 for pos in self.positions[name])
        return done

    def _generate_mask(self):
        if self.discrete:
            # LL-Python API will return an empty dim if there is only 1 agent.
            ndmask = np.array(2 * self.action_size * [False], dtype=np.bool)
            ndmask = np.expand_dims(ndmask, axis=0)
            action_mask = [ndmask]
        else:
            action_mask = None
        return action_mask

    def _compute_reward(self, name: str, done: bool) -> float:
        if done:
            reward = 0.0
            for _pos in self.positions[name]:
                reward += (SUCCESS_REWARD * _pos * self.goal[name]) / len(
                    self.positions[name]
                )
        else:
            reward = -TIME_PENALTY
        return reward

    def _reset_agent(self, name):
        self.goal[name] = self.random.choice([-1, 1])
        self.positions[name] = [0.0 for _ in range(self.action_size)]
        self.step_count[name] = 0
        self.rewards[name] = 0
        self.agent_id[name] = self.agent_id[name] + 1

    def _make_batched_step(
        self, name: str, done: bool, reward: float
    ) -> Tuple[DecisionSteps, TerminalSteps]:
        m_vector_obs = self._make_obs(self.goal[name])
        m_reward = np.array([reward], dtype=np.float32)
        m_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
        action_mask = self._generate_mask()
        decision_step = DecisionSteps(m_vector_obs, m_reward, m_agent_id, action_mask)
        terminal_step = TerminalSteps.empty(self.behavior_spec)
        if done:
            self.final_rewards[name].append(self.rewards[name])
            self._reset_agent(name)
            new_vector_obs = self._make_obs(self.goal[name])
            (
                new_reward,
                new_done,
                new_agent_id,
                new_action_mask,
            ) = self._construct_reset_step(name)

            decision_step = DecisionSteps(
                new_vector_obs, new_reward, new_agent_id, new_action_mask
            )
            terminal_step = TerminalSteps(
                m_vector_obs, m_reward, np.array([False], dtype=np.bool), m_agent_id
            )
        return (decision_step, terminal_step)

    def _construct_reset_step(
        self, name: str
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        new_reward = np.array([0.0], dtype=np.float32)
        new_done = np.array([False], dtype=np.bool)
        new_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
        new_action_mask = self._generate_mask()
        return new_reward, new_done, new_agent_id, new_action_mask

    def step(self) -> None:
        assert all(action is not None for action in self.action.values())
        for name in self.names:

            done = self._take_action(name)
            reward = self._compute_reward(name, done)
            self.rewards[name] += reward
            self.step_result[name] = self._make_batched_step(name, done, reward)

    def reset(self) -> None:  # type: ignore
        for name in self.names:
            self._reset_agent(name)
            self.step_result[name] = self._make_batched_step(name, False, 0.0)

    @property
    def reset_parameters(self) -> Dict[str, str]:
        return {}

    def close(self):
        pass


class HybridEnvironment(SimpleEnvironment):
    def __init__(
        self,
        brain_names,
        step_size=STEP_SIZE,
        num_visual=0,
        num_vector=1,
        vis_obs_size=VIS_OBS_SIZE,
        vec_obs_size=OBS_SIZE,
        continuous_action_size=1,
        discrete_action_size=1,
    ):
        super().__init__(brain_names, False)
        self.continuous_env = SimpleEnvironment(
            brain_names,
            False,
            step_size,
            num_visual,
            num_vector,
            vis_obs_size,
            vec_obs_size,
            continuous_action_size,
        )
        self.discrete_env = SimpleEnvironment(
            brain_names,
            True,
            step_size,
            num_visual,
            num_vector,
            vis_obs_size,
            vec_obs_size,
            discrete_action_size,
        )
        super().__init__(
            brain_names,
            True, # This is needed for env to generate masks correctly
            step_size=step_size,
            num_visual=num_visual,
            num_vector=num_vector,
            action_size=discrete_action_size, # This is needed for env to generate masks correctly
        )
        # Number of steps to reveal the goal for. Lower is harder. Should be
        # less than 1/step_size to force agent to use memory
        self.behavior_spec = HybridBehaviorSpec(
            self._make_obs_spec(), continuous_action_size, tuple(2 for _ in range(discrete_action_size))
        )
        self.continuous_action_size = continuous_action_size
        self.discrete_action_size = discrete_action_size
        self.continuous_action = {}
        self.discrete_action = {}

    def step(self) -> None:
        assert all(action is not None for action in self.continuous_env.action.values())
        assert all(action is not None for action in self.discrete_env.action.values())
        for name in self.names:
            cont_done = self.continuous_env._take_action(name)
            disc_done = self.discrete_env._take_action(name)

            all_done = cont_done and disc_done
            if all_done:
                reward = 0
                for _pos in self.continuous_env.positions[name] + self.discrete_env.positions[name]:
                    reward += (SUCCESS_REWARD * _pos * self.goal[name]) / len(
                        self.continuous_env.positions[name] + self.discrete_env.positions[name]
                    )
            else:
                reward = -TIME_PENALTY
            self.rewards[name] += reward
            self.step_result[name] = self._make_batched_step(
                name, all_done, reward
            )

    def reset(self) -> None:  # type: ignore
        super().reset()
        self.continuous_env.reset()
        self.discrete_env.reset()
        self.continuous_env.goal = self.goal
        self.discrete_env.goal = self.goal

    def set_actions(self, behavior_name: BehaviorName, action) -> None:
        continuous_action = action[:, :self.continuous_action_size]
        discrete_action = action[:, self.continuous_action_size:]
        self.continuous_env.set_actions(behavior_name, continuous_action)
        self.discrete_env.set_actions(behavior_name, discrete_action)


class MemoryEnvironment(SimpleEnvironment):
    def __init__(self, brain_names, use_discrete, step_size=0.2):
        super().__init__(brain_names, use_discrete, step_size=step_size)
        # Number of steps to reveal the goal for. Lower is harder. Should be
        # less than 1/step_size to force agent to use memory
        self.num_show_steps = 2

    def _make_batched_step(
        self, name: str, done: bool, reward: float
    ) -> Tuple[DecisionSteps, TerminalSteps]:
        recurrent_obs_val = (
            self.goal[name] if self.step_count[name] <= self.num_show_steps else 0
        )
        m_vector_obs = self._make_obs(recurrent_obs_val)
        m_reward = np.array([reward], dtype=np.float32)
        m_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
        action_mask = self._generate_mask()
        decision_step = DecisionSteps(m_vector_obs, m_reward, m_agent_id, action_mask)
        terminal_step = TerminalSteps.empty(self.behavior_spec)
        if done:
            self.final_rewards[name].append(self.rewards[name])
            self._reset_agent(name)
            recurrent_obs_val = (
                self.goal[name] if self.step_count[name] <= self.num_show_steps else 0
            )
            new_vector_obs = self._make_obs(recurrent_obs_val)
            (
                new_reward,
                new_done,
                new_agent_id,
                new_action_mask,
            ) = self._construct_reset_step(name)
            decision_step = DecisionSteps(
                new_vector_obs, new_reward, new_agent_id, new_action_mask
            )
            terminal_step = TerminalSteps(
                m_vector_obs, m_reward, np.array([False], dtype=np.bool), m_agent_id
            )
        return (decision_step, terminal_step)


class RecordEnvironment(SimpleEnvironment):
    def __init__(
        self,
        brain_names,
        use_discrete,
        step_size=0.2,
        num_visual=0,
        num_vector=1,
        n_demos=30,
    ):
        super().__init__(
            brain_names,
            use_discrete,
            step_size=step_size,
            num_visual=num_visual,
            num_vector=num_vector,
        )
        self.demonstration_protos: Dict[str, List[AgentInfoActionPairProto]] = {}
        self.n_demos = n_demos
        for name in self.names:
            self.demonstration_protos[name] = []

    def step(self) -> None:
        super().step()
        for name in self.names:
            self.demonstration_protos[name] += proto_from_steps_and_action(
                self.step_result[name][0], self.step_result[name][1], self.action[name]
            )
            self.demonstration_protos[name] = self.demonstration_protos[name][
                -self.n_demos :
            ]

    def solve(self) -> None:
        self.reset()
        for _ in range(self.n_demos):
            for name in self.names:
                if self.discrete:
                    self.action[name] = [[1]] if self.goal[name] > 0 else [[0]]
                else:
                    self.action[name] = [[float(self.goal[name])]]
            self.step()