diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a34055f..4e33292 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -26,7 +26,7 @@ jobs: - name: Install Poetry run: pip install poetry - name: Install Mujoco Dependencies - run: sudo apt install libosmesa6-dev libglfw3 libglew-dev patchelf libgl1-mesa-glx -y + run: sudo apt update && sudo apt install libosmesa6-dev libglfw3 libglew-dev patchelf libgl1-mesa-glx -y - name: Install Mujoco run: | mkdir ~/.mujoco diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..67ae514 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,2 @@ +include mujoco-maze/static/favicon.ico +include mujoco-maze/templates/index.html \ No newline at end of file diff --git a/README.md b/README.md index 51731d2..dcddfb9 100644 --- a/README.md +++ b/README.md @@ -9,14 +9,15 @@ and [openai gym][gym]. Thankfully, this project is based on the code from [rllab] and [tensorflow/models][models]. -Note that [d4rl][d4rl] and [dm_control][dm_control] have similar maze +Note that [d4rl] and [dm_control] have similar maze environments, and you can also check them. But, if you want more customizable or minimal one, I recommend this. ## Usage -Importing `mujoco_maze` registers environments and environments listed -below are available via `gym.make`. +Importing `mujoco_maze` registers environments and you can load +environments by `gym.make`. +All available environments listed are listed in [Environments] section. E.g.,: ```python @@ -116,7 +117,7 @@ not tested well. ## License This project is licensed under Apache License, Version 2.0 -([LICENSE-APACHE](LICENSE) or http://www.apache.org/licenses/LICENSE-2.0). +([LICENSE](LICENSE) or http://www.apache.org/licenses/LICENSE-2.0). [d4rl]: https://github.com/rail-berkeley/d4rl [dm_control]: https://github.com/deepmind/dm_control diff --git a/mujoco_maze/agent_model.py b/mujoco_maze/agent_model.py index d209d78..c749639 100644 --- a/mujoco_maze/agent_model.py +++ b/mujoco_maze/agent_model.py @@ -11,7 +11,7 @@ from gym.utils import EzPickle class AgentModel(ABC, MujocoEnv, EzPickle): FILE: str MANUAL_COLLISION: bool - ORI_IND: int + ORI_IND: Optional[int] = None RADIUS: Optional[float] = None def __init__(self, file_path: str, frame_skip: int) -> None: @@ -27,16 +27,13 @@ class AgentModel(ABC, MujocoEnv, EzPickle): @abstractmethod def _get_obs(self) -> np.ndarray: - """Returns the observation from the model. - """ + """Returns the observation from the model.""" pass def get_xy(self) -> np.ndarray: - """Returns the coordinate of the agent. - """ + """Returns the coordinate of the agent.""" pass def set_xy(self, xy: np.ndarray) -> None: - """Set the coordinate of the agent. - """ + """Set the coordinate of the agent.""" pass diff --git a/mujoco_maze/ant.py b/mujoco_maze/ant.py index 34356e1..01a9e20 100644 --- a/mujoco_maze/ant.py +++ b/mujoco_maze/ant.py @@ -82,7 +82,9 @@ class AntEnv(AgentModel): def reset_model(self): qpos = self.init_qpos + self.np_random.uniform( - size=self.model.nq, low=-0.1, high=0.1, + size=self.model.nq, + low=-0.1, + high=0.1, ) qvel = self.init_qvel + self.np_random.randn(self.model.nv) * 0.1 diff --git a/mujoco_maze/assets/ant.xml b/mujoco_maze/assets/ant.xml old mode 100755 new mode 100644 diff --git a/mujoco_maze/assets/point.xml b/mujoco_maze/assets/point.xml old mode 100755 new mode 100644 diff --git a/mujoco_maze/maze_env.py b/mujoco_maze/maze_env.py index 7619c35..d78b3bb 100644 --- a/mujoco_maze/maze_env.py +++ b/mujoco_maze/maze_env.py @@ -10,7 +10,7 @@ import itertools as it import os import tempfile import xml.etree.ElementTree as ET -from typing import List, Tuple, Type +from typing import Any, List, Optional, Tuple, Type import gym import numpy as np @@ -27,25 +27,20 @@ class MazeEnv(gym.Env): self, model_cls: Type[AgentModel], maze_task: Type[maze_task.MazeTask] = maze_task.MazeTask, - top_down_view: float = False, + include_position: bool = True, maze_height: float = 0.5, maze_size_scaling: float = 4.0, inner_reward_scaling: float = 1.0, restitution_coef: float = 0.8, task_kwargs: dict = {}, - *args, + websock_port: Optional[int] = None, **kwargs, ) -> None: + self.t = 0 # time steps self._task = maze_task(maze_size_scaling, **task_kwargs) - - xml_path = os.path.join(MODEL_DIR, model_cls.FILE) - tree = ET.parse(xml_path) - worldbody = tree.find(".//worldbody") - self._maze_height = height = maze_height self._maze_size_scaling = size_scaling = maze_size_scaling self._inner_reward_scaling = inner_reward_scaling - self.t = 0 # time steps self._observe_blocks = self._task.OBSERVE_BLOCKS self._put_spin_near_agent = self._task.PUT_SPIN_NEAR_AGENT # Observe other objectives @@ -70,11 +65,19 @@ class MazeEnv(gym.Env): if model_cls.RADIUS is None: raise ValueError("Manual collision needs radius of the model") self._collision = maze_env_utils.CollisionDetector( - structure, size_scaling, torso_x, torso_y, model_cls.RADIUS, + structure, + size_scaling, + torso_x, + torso_y, + model_cls.RADIUS, ) # Now all object balls have size=1.0 self._objball_collision = maze_env_utils.CollisionDetector( - structure, size_scaling, torso_x, torso_y, self._task.OBJECT_BALL_SIZE, + structure, + size_scaling, + torso_x, + torso_y, + self._task.OBJECT_BALL_SIZE, ) else: self._collision = None @@ -86,6 +89,11 @@ class MazeEnv(gym.Env): # walls (immovable), chasms (fall), movable blocks self._view = np.zeros([5, 5, 3]) + # Let's create MuJoCo XML + xml_path = os.path.join(MODEL_DIR, model_cls.FILE) + tree = ET.parse(xml_path) + worldbody = tree.find(".//worldbody") + height_offset = 0.0 if self.elevated: # Increase initial z-pos of ant. @@ -141,7 +149,15 @@ class MazeEnv(gym.Env): # Movable block. self.movable_blocks.append(f"movable_{i}_{j}") _add_movable_block( - worldbody, struct, i, j, size_scaling, x, y, h, height_offset, + worldbody, + struct, + i, + j, + size_scaling, + x, + y, + h, + height_offset, ) elif struct.is_object_ball(): # Movable Ball @@ -173,8 +189,11 @@ class MazeEnv(gym.Env): _, file_path = tempfile.mkstemp(text=True, suffix=".xml") tree.write(file_path) self.world_tree = tree - self.wrapped_env = model_cls(*args, file_path=file_path, **kwargs) + self.wrapped_env = model_cls(file_path=file_path, **kwargs) self.observation_space = self._get_obs_space() + self._websock_port = websock_port + self._mj_offscreen_viewer = None + self._websock_server_pipe = None @property def has_extended_obs(self) -> bool: @@ -337,11 +356,32 @@ class MazeEnv(gym.Env): self.data.site_xpos[idx][: len(goal.pos)] = goal.pos @property - def viewer(self): - return self.wrapped_env.viewer + def viewer(self) -> Any: + if self._websock_port is not None: + return self._mj_viewer + else: + return self.wrapped_env.viewer - def render(self, *args, **kwargs): - return self.wrapped_env.render(*args, **kwargs) + def _render_image(self) -> np.ndarray: + self._mj_offscreen_viewer._set_mujoco_buffers() + self._mj_offscreen_viewer.render(640, 480) + return np.asarray( + self._mj_offscreen_viewer.read_pixels(640, 480, depth=False)[::-1, :, :], + dtype=np.uint8, + ) + + def render(self, mode="human", **kwargs) -> Optional[np.ndarray]: + if mode == "human" and self._websock_port is not None: + if self._mj_offscreen_viewer is None: + from mujoco_py import MjRenderContextOffscreen as MjRCO + + from mujoco_maze.websock_viewer import start_server + + self._mj_offscreen_viewer = MjRCO(self.wrapped_env.sim) + self._websock_server_pipe = start_server(self._websock_port) + self._websock_server_pipe.send(self._render_image()) + else: + return self.wrapped_env.render(mode, **kwargs) @property def action_space(self): @@ -406,6 +446,8 @@ class MazeEnv(gym.Env): def close(self) -> None: self.wrapped_env.close() + if self._websock_server_pipe is not None: + self._websock_server_pipe.send(None) def _add_object_ball( @@ -479,7 +521,10 @@ def _add_movable_block( shrink = 1.0 size = size_scaling * 0.5 * shrink movable_body = ET.SubElement( - worldbody, "body", name=f"movable_{i}_{j}", pos=f"{x} {y} {h}", + worldbody, + "body", + name=f"movable_{i}_{j}", + pos=f"{x} {y} {h}", ) ET.SubElement( movable_body, diff --git a/mujoco_maze/maze_env_utils.py b/mujoco_maze/maze_env_utils.py index 348b88c..21f35d1 100644 --- a/mujoco_maze/maze_env_utils.py +++ b/mujoco_maze/maze_env_utils.py @@ -13,7 +13,7 @@ from typing import Any, List, Optional, Sequence, Tuple, Union import numpy as np Self = Any -Point = np.complex +Point = complex class MazeCell(Enum): @@ -83,13 +83,15 @@ class MazeCell(Enum): class Line: def __init__( - self, p1: Union[Sequence[float], Point], p2: Union[Sequence[float], Point], + self, + p1: Union[Sequence[float], Point], + p2: Union[Sequence[float], Point], ) -> None: - self.p1 = p1 if isinstance(p1, Point) else np.complex(*p1) - self.p2 = p2 if isinstance(p2, Point) else np.complex(*p2) + self.p1 = p1 if isinstance(p1, Point) else complex(*p1) + self.p2 = p2 if isinstance(p2, Point) else complex(*p2) self.v1 = self.p2 - self.p1 - self.conj_v1 = np.conjugate(self.v1) - self.norm = np.absolute(self.v1) + self.conj_v1 = self.v1.conjugate() + self.norm = abs(self.v1) def _intersect(self, other: Self) -> bool: v2 = other.p1 - self.p1 @@ -98,15 +100,15 @@ class Line: def _projection(self, p: Point) -> Point: nv1 = -self.v1 - nv1_norm = np.absolute(nv1) ** 2 - scale = np.real(np.conjugate(p - self.p1) * nv1) / nv1_norm + nv1_norm = abs(nv1) ** 2 + scale = ((p - self.p1).conjugate() * nv1).real / nv1_norm return self.p1 + nv1 * scale def reflection(self, p: Point) -> Point: return p + 2.0 * (self._projection(p) - p) def distance(self, p: Point) -> float: - return np.absolute(p - self._projection(p)) + return abs(p - self._projection(p)) def intersect(self, other: Self) -> Point: if self._intersect(other) and other._intersect(self): @@ -141,8 +143,7 @@ class Collision: class CollisionDetector: - """For manual collision detection. - """ + """For manual collision detection.""" EPS: float = 0.05 NEIGHBORS: List[Tuple[int, int]] = [[0, -1], [-1, 0], [0, 1], [1, 0]] @@ -197,9 +198,9 @@ class CollisionDetector: if len(collisions) == 0: return None col = collisions[0] - dist = np.absolute(col._point - move.p1) + dist = abs(col._point - move.p1) for collision in collisions[1:]: - new_dist = np.absolute(collision._point - move.p1) + new_dist = abs(collision._point - move.p1) if new_dist < dist: col, dist = collision, new_dist return col diff --git a/mujoco_maze/maze_task.py b/mujoco_maze/maze_task.py index e953cb1..21b4ce6 100644 --- a/mujoco_maze/maze_task.py +++ b/mujoco_maze/maze_task.py @@ -145,6 +145,37 @@ class DistRewardSimpleRoom(GoalRewardSimpleRoom, DistRewardMixIn): pass +class GoalRewardSquareRoom(GoalRewardUMaze): + MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 2.0) + + def __init__(self, scale: float, goal: Tuple[float, float] = (1.0, 0.0)) -> None: + super().__init__(scale) + self.goals = [MazeGoal(np.array(goal) * scale)] + + @staticmethod + def create_maze() -> List[List[MazeCell]]: + E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT + return [ + [B, B, B, B, B], + [B, E, E, E, B], + [B, E, R, E, B], + [B, E, E, E, B], + [B, B, B, B, B], + ] + + +class NoRewardSquareRoom(GoalRewardSimpleRoom): + def __init__(self, scale: float) -> None: + super().__init__(scale) + + def reward(self, _obs: np.ndarray) -> float: + return 0.0 + + +class DistRewardSquareRoom(GoalRewardSquareRoom, DistRewardMixIn): + pass + + class GoalRewardPush(GoalRewardUMaze): OBSERVE_BLOCKS: bool = True @@ -329,6 +360,48 @@ class SubGoalTRoom(GoalRewardTRoom): ) +class NoRewardCorridor(MazeTask): + REWARD_THRESHOLD: float = 0.0 + MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 1.0) + + def reward(self, _obs: np.ndarray) -> float: + return 0.0 + + @staticmethod + def create_maze() -> List[List[MazeCell]]: + E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT + return [ + [B, B, B, B, B, B, B, B, B], + [B, E, E, B, E, E, E, E, B], + [B, E, E, B, E, E, E, E, B], + [B, E, E, E, E, E, B, B, B], + [B, E, E, E, R, E, E, E, B], + [B, B, B, E, E, E, E, E, B], + [B, E, E, E, E, B, E, E, B], + [B, E, E, E, E, B, E, E, B], + [B, B, B, B, B, B, B, B, B], + ] + + +class GoalRewardCorridor(NoRewardCorridor): + REWARD_THRESHOLD: float = 0.9 + PENALTY: float = -0.0001 + + def __init__(self, scale: float, goal: Tuple[float, float] = (3.0, -3.0)) -> None: + super().__init__(scale) + self.goals.append(MazeGoal(np.array(goal) * scale)) + + def reward(self, obs: np.ndarray) -> float: + for goal in self.goals: + if goal.neighbor(obs): + return goal.reward_scale + return self.PENALTY + + +class DistRewardCorridor(GoalRewardCorridor, DistRewardMixIn): + pass + + class GoalRewardBlockMaze(GoalRewardUMaze): MAZE_SIZE_SCALING: Scaling = Scaling(8.0, 4.0, None) OBSERVE_BLOCKS: bool = True @@ -406,6 +479,14 @@ class DistRewardBilliard(GoalRewardBilliard): return -self.goals[0].euc_dist(obs[3:6]) / self.scale +class NoRewardBilliard(GoalRewardBilliard): + def __init__(self, scale: float) -> None: + MazeTask.__init__(self, scale) + + def reward(self, _obs: np.ndarray) -> float: + return 0.0 + + class SubGoalBilliard(GoalRewardBilliard): def __init__( self, @@ -453,6 +534,7 @@ class BanditBilliard(SubGoalBilliard): class TaskRegistry: REGISTRY: Dict[str, List[Type[MazeTask]]] = { "SimpleRoom": [DistRewardSimpleRoom, GoalRewardSimpleRoom], + "SquareRoom": [DistRewardSquareRoom, GoalRewardSquareRoom, NoRewardSquareRoom], "UMaze": [DistRewardUMaze, GoalRewardUMaze], "Push": [DistRewardPush, GoalRewardPush], "Fall": [DistRewardFall, GoalRewardFall], @@ -460,11 +542,13 @@ class TaskRegistry: "4Rooms": [DistReward4Rooms, GoalReward4Rooms, SubGoal4Rooms], "TRoom": [DistRewardTRoom, GoalRewardTRoom, SubGoalTRoom], "BlockMaze": [DistRewardBlockMaze, GoalRewardBlockMaze], + "Corridor": [DistRewardCorridor, GoalRewardCorridor, NoRewardCorridor], "Billiard": [ - DistRewardBilliard, - GoalRewardBilliard, - SubGoalBilliard, - BanditBilliard, + DistRewardBilliard, # v0 + GoalRewardBilliard, # v1 + SubGoalBilliard, # v2 + BanditBilliard, # v3 + NoRewardBilliard, # v4 ], } diff --git a/mujoco_maze/point.py b/mujoco_maze/point.py index 4e8c071..6f64603 100644 --- a/mujoco_maze/point.py +++ b/mujoco_maze/point.py @@ -22,7 +22,7 @@ class PointEnv(AgentModel): VELOCITY_LIMITS: float = 10.0 - def __init__(self, file_path: Optional[str] = None): + def __init__(self, file_path: Optional[str] = None) -> None: super().__init__(file_path, 1) high = np.inf * np.ones(6, dtype=np.float32) high[3:] = self.VELOCITY_LIMITS * 1.2 diff --git a/mujoco_maze/reacher.py b/mujoco_maze/reacher.py index 7a97118..d94cb4a 100644 --- a/mujoco_maze/reacher.py +++ b/mujoco_maze/reacher.py @@ -1,7 +1,7 @@ """ Based on the reacher in `dm_control`_. -.. _gym: https://github.com/openai/gym +.. _dm_control: https://github.com/deepmind/dm_control """ from typing import Tuple @@ -54,10 +54,14 @@ class ReacherEnv(AgentModel): def reset_model(self) -> np.ndarray: qpos = self.init_qpos + self.np_random.uniform( - low=-0.1, high=0.1, size=self.model.nq, + low=-0.1, + high=0.1, + size=self.model.nq, ) qvel = self.init_qvel + self.np_random.uniform( - low=-0.1, high=0.1, size=self.model.nv, + low=-0.1, + high=0.1, + size=self.model.nv, ) self.set_state(qpos, qvel) diff --git a/mujoco_maze/swimmer.py b/mujoco_maze/swimmer.py index ef56e89..efe225a 100644 --- a/mujoco_maze/swimmer.py +++ b/mujoco_maze/swimmer.py @@ -37,7 +37,6 @@ class SwimmerEnv(AgentModel): def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, dict]: xy_pos_before = self.sim.data.qpos[:2].copy() self.do_simulation(action, self.frame_skip) - forward_reward = self._forward_reward(xy_pos_before) ctrl_cost = self._ctrl_cost_weight * np.sum(np.square(action)) return ( @@ -55,10 +54,14 @@ class SwimmerEnv(AgentModel): def reset_model(self) -> np.ndarray: qpos = self.init_qpos + self.np_random.uniform( - low=-0.1, high=0.1, size=self.model.nq, + low=-0.1, + high=0.1, + size=self.model.nq, ) qvel = self.init_qvel + self.np_random.uniform( - low=-0.1, high=0.1, size=self.model.nv, + low=-0.1, + high=0.1, + size=self.model.nv, ) self.set_state(qpos, qvel) diff --git a/mujoco_maze/websock_viewer.py b/mujoco_maze/websock_viewer.py new file mode 100644 index 0000000..647d5e1 --- /dev/null +++ b/mujoco_maze/websock_viewer.py @@ -0,0 +1,116 @@ +import asyncio +import io +import multiprocessing as mp + +import fastapi +import uvicorn +from PIL import Image + + +HTML = """ + + + + MuJoCo maze visualizer + + +

MuJoCo Maze Visualizer

+ +
+ +
+ Video + + +""" + + +class _ServerWorker(mp.Process): + def __init__(self, pipe: mp.connection.Pipe, port: int) -> None: + super().__init__() + self.pipe = pipe + self.port = port + self.video_frames = [] + + def _run_server(self) -> None: + + app = fastapi.FastAPI() + html = HTML.replace("{{port}}", str(self.port)) + + @app.get("/") + async def root(): + return fastapi.responses.HTMLResponse(html) + + server = None + + @app.websocket("/ws") + async def ws(websocket: fastapi.WebSocket): + await websocket.accept() + loop = asyncio.get_running_loop() + while True: + image_array = await loop.run_in_executor(None, self.pipe.recv) + if image_array is None: + break + self.video_frames.append(image_array) + image = Image.fromarray(image_array) + with io.BytesIO() as stream: + image.save(stream, format="png") + res = stream.getvalue() + await websocket.send_bytes(res) + await websocket.close() + server.should_exit = True + + @app.get("/video") + async def video(): + import imageio + + writer = imageio.get_writer("/tmp/mujoco-maze-video.mp4") + for frame in self.video_frames: + writer.append_data(frame) + writer.close() + video = open("/tmp/mujoco-maze-video.mp4", mode="rb") + return fastapi.responses.StreamingResponse(video, media_type="video/mp4") + + config = uvicorn.Config(app, port=self.port) + server = uvicorn.Server(config) + server.run() + + def run(self) -> None: + try: + self._run_server() + except KeyboardInterrupt: + pass + except Exception as e: + print("Exception in websocket server") + raise e + + +def start_server(port: int) -> mp.connection.Connection: + mainproc_pipe, server_pipe = mp.Pipe() + worker = _ServerWorker(server_pipe, port) + worker.start() + return mainproc_pipe diff --git a/poetry.lock b/poetry.lock index 5faf0de..0e4800d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,117 +1,109 @@ [[package]] -category = "dev" -description = "Atomic file writes." name = "atomicwrites" +version = "1.4.0" +description = "Atomic file writes." +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "1.4.0" [[package]] -category = "dev" -description = "Classes Without Boilerplate" name = "attrs" +version = "21.2.0" +description = "Classes Without Boilerplate" +category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "20.2.0" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [package.extras] -dev = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "sphinx", "sphinx-rtd-theme", "pre-commit"] -docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"] -tests = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"] -tests_no_zope = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"] +dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", 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