add gym_pybullet_drones env

README.md

@@ -117,6 +117,7 @@ Environments currently supported by OpenRL (for more details, please refer to [G
 - [Omniverse Isaac Gym](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs)
 - [DeepMind Control](https://shimmy.farama.org/environments/dm_control/)
 - [Snake](http://www.jidiai.cn/env_detail?envid=1)
+- [gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones)
 - [GridWorld](./examples/gridworld/)
 - [Super Mario Bros](https://github.com/Kautenja/gym-super-mario-bros)
 - [Gym Retro](https://github.com/openai/retro)

README_zh.md

@@ -93,6 +93,7 @@ OpenRL目前支持的环境（更多详情请参考 [Gallery](Gallery.md)）：
 - [Omniverse Isaac Gym](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs)
 - [DeepMind Control](https://shimmy.farama.org/environments/dm_control/)
 - [Snake](http://www.jidiai.cn/env_detail?envid=1)
+- [gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones)
 - [GridWorld](./examples/gridworld/)
 - [Super Mario Bros](https://github.com/Kautenja/gym-super-mario-bros)
 - [Gym Retro](https://github.com/openai/retro)

examples/gym_pybullet_drones/README.md

@@ -0,0 +1,8 @@
+
+### Installation
+
+- Python >= 3.10
+- Fellow the installation instruction of [gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones#installation).
+
+### Train PPO
+

examples/gym_pybullet_drones/ppo.yaml

@@ -0,0 +1,10 @@
+episode_length: 500
+lr: 1e-3
+critic_lr: 1e-3
+gamma: 0.1
+ppo_epoch: 5
+use_valuenorm: true
+entropy_coef: 0.0
+hidden_size: 128
+layer_N: 4
+use_recurrent_policy: true

examples/gym_pybullet_drones/test_env.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+import time
+
+import gym_pybullet_drones
+import gymnasium as gym
+import numpy as np
+
+from openrl.envs.common import make
+
+
+def test_env():
+    env = gym.make("hover-aviary-v0", gui=False, record=False)
+    print("obs space:", env.observation_space)
+    print("action space:", env.action_space)
+    obs, info = env.reset(seed=42, options={})
+    totoal_step = 0
+    totol_reward = 0.0
+    while True:
+        obs, reward, done, truncated, info = env.step(env.action_space.sample())
+        totoal_step += 1
+        totol_reward += reward
+        # env.render()
+        # time.sleep(1)
+        if done:
+            break
+    print("total step:", totoal_step)
+    print("total reward:", totol_reward)
+
+
+def test_vec_env():
+    env = make(
+        "pybullet_drones/hover-aviary-v0",
+        env_num=2,
+        gui=False,
+        record=False,
+        asynchronous=True,
+    )
+    info, obs = env.reset(seed=0)
+    totoal_step = 0
+    totol_reward = 0.0
+    while True:
+        obs, reward, done, info = env.step(env.random_action())
+        totoal_step += 1
+        totol_reward += np.mean(reward)
+        if np.any(done) or totoal_step > 100:
+            break
+    env.close()
+    print("total step:", totoal_step)
+    print("total reward:", totol_reward)
+
+
+if __name__ == "__main__":
+    test_env()
+    # test_vec_env()

examples/gym_pybullet_drones/train_ppo.py

@@ -0,0 +1,89 @@
+import numpy as np
+import torch
+
+from openrl.configs.config import create_config_parser
+from openrl.envs.common import make
+from openrl.modules.common import PPONet as Net
+from openrl.runners.common import PPOAgent as Agent
+
+env_name = "pybullet_drones/hover-aviary-v0"
+
+
+def train():
+    # create the neural network
+    cfg_parser = create_config_parser()
+    cfg = cfg_parser.parse_args(["--config", "ppo.yaml"])
+
+    # create environment, set environment parallelism to 64
+    env_num = 20
+    # env_num = 1
+
+    env = make(
+        env_name,
+        env_num=env_num,
+        cfg=cfg,
+        asynchronous=True,
+        env_wrappers=[],
+        gui=False,
+    )
+
+    net = Net(env, cfg=cfg, device="cuda" if torch.cuda.is_available() else "cpu")
+    # initialize the trainer
+    agent = Agent(
+        net,
+    )
+    # start training, set total number of training steps to 100000
+    agent.train(total_time_steps=1000000)
+
+    agent.save("./ppo_agent")
+    env.close()
+    return agent
+
+
+def evaluation():
+    cfg_parser = create_config_parser()
+    cfg = cfg_parser.parse_args(["--config", "ppo.yaml"])
+    # begin to test
+    # Create an environment for testing and set the number of environments to interact with to 4. Set rendering mode to group_rgb_array.
+
+    env = make(
+        env_name,
+        env_num=1,
+        asynchronous=False,
+        env_wrappers=[],
+        cfg=cfg,
+        gui=False,
+        record=False,
+    )
+
+    net = Net(env, cfg=cfg, device="cuda" if torch.cuda.is_available() else "cpu")
+    # initialize the trainer
+    agent = Agent(
+        net,
+    )
+    agent.load("./ppo_agent")
+
+    # The trained agent sets up the interactive environment it needs.
+    agent.set_env(env)
+    # Initialize the environment and get initial observations and environmental information.
+    obs, info = env.reset()
+    done = False
+    step = 0
+    total_reward = 0.0
+    while not np.any(done):
+        # Based on environmental observation input, predict next action.
+        action, _ = agent.act(obs, deterministic=True)
+        print("action:", action)
+        obs, r, done, info = env.step(action)
+        step += 1
+        total_reward += np.mean(r)
+        # if step % 50 == 0:
+        #     print(f"{step}: reward:{np.mean(r)}")
+    print("total step:", step)
+    print("total reward:", total_reward)
+    env.close()
+
+
+if __name__ == "__main__":
+    # train()
+    evaluation()

openrl/envs/common/registration.py

@@ -65,7 +65,14 @@ def make(
             id=id, env_num=env_num, render_mode=convert_render_mode, **kwargs
         )
     else:
-        if id.startswith("snakes_"):
+        if id.startswith("pybullet_drones/"):
+            from openrl.envs.gym_pybullet_drones import make_single_agent_drone_envs
+
+            env_fns = make_single_agent_drone_envs(
+                id=id, env_num=env_num, render_mode=convert_render_mode, **kwargs
+            )
+
+        elif id.startswith("snakes_"):
             from openrl.envs.snake import make_snake_envs
 
             env_fns = make_snake_envs(

openrl/envs/gym_pybullet_drones/__init__.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+import copy
+from typing import Callable, List, Optional, Union
+
+import gymnasium as gym
+from gymnasium import Env
+
+from openrl.envs.common import build_envs
+
+
+def make_single_agent_drone_env(id: str, render_mode, disable_env_checker, **kwargs):
+    import gym_pybullet_drones
+
+    prefix = "pybullet_drones/"
+    assert id.startswith(prefix), "id must start with pybullet_drones/"
+    kwargs.pop("cfg")
+
+    env = gym.envs.registration.make(id[len(prefix) :], **kwargs)
+    return env
+
+
+def make_single_agent_drone_envs(
+    id: str,
+    env_num: int = 1,
+    render_mode: Optional[Union[str, List[str]]] = None,
+    **kwargs,
+) -> List[Callable[[], Env]]:
+    from openrl.envs.wrappers import (  # AutoReset,; DictWrapper,
+        RemoveTruncated,
+        Single2MultiAgentWrapper,
+    )
+
+    env_wrappers = copy.copy(kwargs.pop("env_wrappers", []))
+    env_wrappers += [
+        Single2MultiAgentWrapper,
+        RemoveTruncated,
+    ]
+
+    env_fns = build_envs(
+        make=make_single_agent_drone_env,
+        id=id,
+        env_num=env_num,
+        render_mode=render_mode,
+        wrappers=env_wrappers,
+        **kwargs,
+    )
+    return env_fns