gym-pybullet-drones
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control (by utiasDSL)
on-policy
This is the official implementation of Multi-Agent PPO (MAPPO). (by marlbenchmark)
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| gym-pybullet-drones | on-policy | |
|---|---|---|
| 4 | 12 | |
| 1,083 | 1,125 | |
| 6.8% | 7.8% | |
| 8.4 | 4.9 | |
| 6 days ago | 12 days ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gym-pybullet-drones
Posts with mentions or reviews of gym-pybullet-drones.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-26.
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Drone Racing RL Environments
Gym-pybullet-drones (https://github.com/utiasDSL/gym-pybullet-drones)
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drone environment ?
https://github.com/utiasDSL/gym-pybullet-drones I have used this library and liked it a lot. It comes with a ready quadcopter and environment. I think at the initialization step you should be able to apply random force for throwing effect.
- How to simulate solidworks model with python?
- Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control
on-policy
Posts with mentions or reviews of on-policy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-03.
- How do you compute rewards when you are using parallel environments?
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Renderer of the environment does not work?
I am trying to feed the agents with visual observation and thus using the renderer of this environment (https://github.com/marlbenchmark/on-policy/blob/main/onpolicy/envs/mpe/rendering.py), but I get this as an image:
- Stuck on this error for days: I can't use importlib the right way
- Difference between setup.py, environments.yaml and requirements.txt
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Ubuntu terminal crashes when I launch a deep reinforcement learning model
I am trying to run this code on my Ubuntu machine (https://github.com/marlbenchmark/on-policy).
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"chmod" is not recognized as an internal or external command, operable program or batch file
If you don't want to install a Linux VM, the other option is to read the source of the train_mpe.sh script and write your own version as a Windows batch file.
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Confused between "centralized critic" and "centralized training decentralized execution"
Sorry, this was the paper: https://arxiv.org/abs/2104.07750 But I guess you already answered my question. Indeed, agents receive a global obervation, but cannot directly observe other agents' actions, states, orrewards, and do not share parameters. So if I understand correctly that what they're using here is independent PPO with global observation, but no centralized critic. Which is what MAPPO (https://github.com/marlbenchmark/on-policy/blob/main/onpolicy/algorithms/r_mappo/algorithm/r_actor_critic.py) does: centralized observation space, but (if I'm correct), decentralized critic.
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Why is this implementation of PPO using a replay buffer?
I don't see the buffer being cleared anywhere, but it looks to me like it may not need to... For example, the implementation of SeparatedReplayBuffer receives the episode_length (or "horizon" as is sometimes called) and sets the size of the buffer accordingly when its initialized. That way, the amount of samples collected before each policy/value update is constant. You just need one giant tensor block to collect all your samples, then after doing a networks update, why clear them out? Just overwrite the existing samples, since you know you'll collect exactly the same number of new samples.
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MARL top conference papers are ridiculous
https://github.com/marlbenchmark/on-policy (MAPPO-FP)
What are some alternatives?
When comparing gym-pybullet-drones and on-policy you can also consider the following projects:
robot-gym - RL applied to robotics.
DI-engine - OpenDILab Decision AI Engine
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
auto-sklearn - Automated Machine Learning with scikit-learn
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Bullet - Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
isaac_ros_apriltag - Hardware-accelerated Apriltag detection and pose estimation.
flightmare - An Open Flexible Quadrotor Simulator
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
HighwayEnv - A minimalist environment for decision-making in autonomous driving
AirSim-Drone-Racing-Lab - A framework for drone racing research, built on Microsoft AirSim.
AirSim-NeurIPS2019-Drone-Racing - Drone Racing @ NeurIPS 2019, built on Microsoft AirSim
gym-pybullet-drones vs robot-gym
on-policy vs DI-engine
gym-pybullet-drones vs pybullet-gym
on-policy vs auto-sklearn
gym-pybullet-drones vs rl-baselines3-zoo
gym-pybullet-drones vs Bullet
gym-pybullet-drones vs isaac_ros_apriltag
gym-pybullet-drones vs flightmare
gym-pybullet-drones vs drl_grasping
gym-pybullet-drones vs HighwayEnv
gym-pybullet-drones vs AirSim-Drone-Racing-Lab
gym-pybullet-drones vs AirSim-NeurIPS2019-Drone-Racing