Suggestion on unittest for ding/utils/plot.py

Updated on 2024.02.04 DI-engine-v0.5.1

Introduction to DI-engine

DI-engine is a generalized decision intelligence engine for PyTorch and JAX.

It provides python-first and asynchronous-native task and middleware abstractions, and modularly integrates several of the most important decision-making concepts: Env, Policy and Model. Based on the above mechanisms, DI-engine supports various deep reinforcement learning algorithms with superior performance, high efficiency, well-organized documentation and unittest:

Most basic DRL algorithms: such as DQN, Rainbow, PPO, TD3, SAC, R2D2, IMPALA
Multi-agent RL algorithms: such as QMIX, WQMIX, MAPPO, HAPPO, ACE
Imitation learning algorithms (BC/IRL/GAIL): such as GAIL, SQIL, Guided Cost Learning, Implicit BC
Offline RL algorithms: BCQ, CQL, TD3BC, Decision Transformer, EDAC, Diffuser, Decision Diffuser, SO2
Model-based RL algorithms: SVG, STEVE, MBPO, DDPPO, DreamerV3, MuZero
Exploration algorithms: HER, RND, ICM, NGU
LLM + RL Algorithms: PPO-max, DPO, MODPO，PromptPG
Other algorithms: such as PER, PLR, PCGrad

DI-engine aims to standardize different Decision Intelligence environments and applications, supporting both academic research and prototype applications. Various training pipelines and customized decision AI applications are also supported:

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Traditional academic environments
- DI-zoo: various decision intelligence demonstrations and benchmark environments with DI-engine.
Tutorial courses
- PPOxFamily: PPO x Family DRL Tutorial Course
Real world decision AI applications
- DI-star: Decision AI in StarCraftII
- DI-drive: Auto-driving platform
- DI-sheep: Decision AI in 3 Tiles Game
- DI-smartcross: Decision AI in Traffic Light Control
- DI-bioseq: Decision AI in Biological Sequence Prediction and Searching
- DI-1024: Deep Reinforcement Learning + 1024 Game
Research paper
- InterFuser: [CoRL 2022] Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
- ACE: [AAAI 2023] ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
- GoBigger: [ICLR 2023] Multi-Agent Decision Intelligence Environment
- DOS: [CVPR 2023] ReasonNet: End-to-End Driving with Temporal and Global Reasoning
- LightZero: [NeurIPS 2023 Spotlight] A lightweight and efficient MCTS/AlphaZero/MuZero algorithm toolkit
- SO2: [AAAI 2024] A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
- LMDrive: LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Docs and Tutorials
- DI-engine-docs: Tutorials, best practice and the API reference.
- awesome-model-based-RL: A curated list of awesome Model-Based RL resources
- awesome-exploration-RL: A curated list of awesome exploration RL resources
- awesome-decision-transformer: A curated list of Decision Transformer resources
- awesome-RLHF: A curated list of reinforcement learning with human feedback resources
- awesome-multi-modal-reinforcement-learning: A curated list of Multi-Modal Reinforcement Learning resources
- awesome-AI-based-protein-design: a collection of research papers for AI-based protein design
- awesome-diffusion-model-in-rl: A curated list of Diffusion Model in RL resources
- awesome-end-to-end-autonomous-driving: A curated list of awesome End-to-End Autonomous Driving resources
- awesome-driving-behavior-prediction: A collection of research papers for Driving Behavior Prediction

On the low-level end, DI-engine comes with a set of highly re-usable modules, including RL optimization functions, PyTorch utilities and auxiliary tools.

BTW, DI-engine also has some special system optimization and design for efficient and robust large-scale RL training:

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treevalue: Tree-nested data structure
DI-treetensor: Tree-nested PyTorch tensor Lib
DI-toolkit: A simple toolkit package for decision intelligence
DI-orchestrator: RL Kubernetes Custom Resource and Operator Lib
DI-hpc: RL HPC OP Lib
DI-store: RL Object Store

Have fun with exploration and exploitation.

Installation

You can simply install DI-engine from PyPI with the following command:

pip install DI-engine

If you use Anaconda or Miniconda, you can install DI-engine from conda-forge through the following command:

conda install -c opendilab di-engine

For more information about installation, you can refer to installation.

And our dockerhub repo can be found here，we prepare base image and env image with common RL environments.

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base: opendilab/ding:nightly
rpc: opendilab/ding:nightly-rpc
atari: opendilab/ding:nightly-atari
mujoco: opendilab/ding:nightly-mujoco
dmc: opendilab/ding:nightly-dmc2gym
metaworld: opendilab/ding:nightly-metaworld
smac: opendilab/ding:nightly-smac
grf: opendilab/ding:nightly-grf
cityflow: opendilab/ding:nightly-cityflow
evogym: opendilab/ding:nightly-evogym
d4rl: opendilab/ding:nightly-d4rl

The detailed documentation are hosted on doc | 中文文档.

Quick Start

3 Minutes Kickoff

3 Minutes Kickoff (colab)

DI-engine Huggingface Kickoff (colab)

How to migrate a new RL Env | 如何迁移一个新的强化学习环境

How to customize the neural network model | 如何定制策略使用的神经网络模型

测试/部署 强化学习策略 的样例

新老 pipeline 的异同对比

Feature

Algorithm Versatility

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discrete means discrete action space, which is only label in normal DRL algorithms (1-23)

means continuous action space, which is only label in normal DRL algorithms (1-23)

means hybrid (discrete + continuous) action space (1-23)

Distributed Reinforcement Learning｜分布式强化学习

Multi-Agent Reinforcement Learning｜多智能体强化学习

Exploration Mechanisms in Reinforcement Learning｜强化学习中的探索机制

Imitation Learning｜模仿学习

Offiline Reinforcement Learning｜离线强化学习

Model-Based Reinforcement Learning｜基于模型的强化学习

means other sub-direction algorithms, usually as plugin-in in the whole pipeline

P.S: The .py file in Runnable Demo can be found in dizoo

No.	Algorithm	Doc and Implementation	Runnable Demo
1	DQN	DQN doc DQN中文文档 policy/dqn	python3 -u cartpole_dqn_main.py / ding -m serial -c cartpole_dqn_config.py -s 0
2	C51	C51 doc policy/c51	ding -m serial -c cartpole_c51_config.py -s 0
3	QRDQN	QRDQN doc policy/qrdqn	ding -m serial -c cartpole_qrdqn_config.py -s 0
4	IQN	IQN doc policy/iqn	ding -m serial -c cartpole_iqn_config.py -s 0
5	FQF	FQF doc policy/fqf	ding -m serial -c cartpole_fqf_config.py -s 0
6	Rainbow	Rainbow doc policy/rainbow	ding -m serial -c cartpole_rainbow_config.py -s 0
7	SQL	SQL doc policy/sql	ding -m serial -c cartpole_sql_config.py -s 0
8	R2D2	R2D2 doc policy/r2d2	ding -m serial -c cartpole_r2d2_config.py -s 0
9	PG	PG doc policy/pg	ding -m serial -c cartpole_pg_config.py -s 0
10	PromptPG	policy/prompt_pg	ding -m serial_onpolicy -c tabmwp_pg_config.py -s 0
11	A2C	A2C doc policy/a2c	ding -m serial -c cartpole_a2c_config.py -s 0
12	PPO/MAPPO	PPO doc policy/ppo	python3 -u cartpole_ppo_main.py / ding -m serial_onpolicy -c cartpole_ppo_config.py -s 0
13	PPG	PPG doc policy/ppg	python3 -u cartpole_ppg_main.py
14	ACER	ACER doc policy/acer	ding -m serial -c cartpole_acer_config.py -s 0
15	IMPALA	IMPALA doc policy/impala	ding -m serial -c cartpole_impala_config.py -s 0
16	DDPG/PADDPG	DDPG doc policy/ddpg	ding -m serial -c pendulum_ddpg_config.py -s 0
17	TD3	TD3 doc policy/td3	python3 -u pendulum_td3_main.py / ding -m serial -c pendulum_td3_config.py -s 0
18	D4PG	D4PG doc policy/d4pg	python3 -u pendulum_d4pg_config.py
19	SAC/[MASAC]	SAC doc policy/sac	ding -m serial -c pendulum_sac_config.py -s 0
20	PDQN	policy/pdqn	ding -m serial -c gym_hybrid_pdqn_config.py -s 0
21	MPDQN	policy/pdqn	ding -m serial -c gym_hybrid_mpdqn_config.py -s 0
22	HPPO	policy/ppo	ding -m serial_onpolicy -c gym_hybrid_hppo_config.py -s 0
23	BDQ	policy/bdq	python3 -u hopper_bdq_config.py
24	MDQN	policy/mdqn	python3 -u asterix_mdqn_config.py
25	QMIX	QMIX doc policy/qmix	ding -m serial -c smac_3s5z_qmix_config.py -s 0
26	COMA	COMA doc policy/coma	ding -m serial -c smac_3s5z_coma_config.py -s 0
27	QTran	policy/qtran	ding -m serial -c smac_3s5z_qtran_config.py -s 0
28	WQMIX	WQMIX doc policy/wqmix	ding -m serial -c smac_3s5z_wqmix_config.py -s 0
29	CollaQ	CollaQ doc policy/collaq	ding -m serial -c smac_3s5z_collaq_config.py -s 0
30	MADDPG	MADDPG doc policy/ddpg	ding -m serial -c ptz_simple_spread_maddpg_config.py -s 0
31	GAIL	GAIL doc reward_model/gail	ding -m serial_gail -c cartpole_dqn_gail_config.py -s 0
32	SQIL	SQIL doc entry/sqil	ding -m serial_sqil -c cartpole_sqil_config.py -s 0
33	DQFD	DQFD doc policy/dqfd	ding -m serial_dqfd -c cartpole_dqfd_config.py -s 0
34	R2D3	R2D3 doc R2D3中文文档 policy/r2d3	python3 -u pong_r2d3_r2d2expert_config.py
35	Guided Cost Learning	Guided Cost Learning中文文档 reward_model/guided_cost	python3 lunarlander_gcl_config.py
36	TREX	TREX doc reward_model/trex	python3 mujoco_trex_main.py
37	Implicit Behavorial Cloning (DFO+MCMC)	policy/ibc model/template/ebm	python3 d4rl_ibc_main.py -s 0 -c pen_human_ibc_mcmc_config.py
38	BCO	entry/bco	python3 -u cartpole_bco_config.py
39	HER	HER doc reward_model/her	python3 -u bitflip_her_dqn.py
40	RND	RND doc reward_model/rnd	python3 -u cartpole_rnd_onppo_config.py
41	ICM	ICM doc ICM中文文档 reward_model/icm	python3 -u cartpole_ppo_icm_config.py
42	CQL	CQL doc policy/cql	python3 -u d4rl_cql_main.py
43	TD3BC	TD3BC doc policy/td3_bc	python3 -u d4rl_td3_bc_main.py
44	Decision Transformer	policy/dt	python3 -u d4rl_dt_mujoco.py
45	EDAC	EDAC doc policy/edac	python3 -u d4rl_edac_main.py
46	QGPO	QGPO doc policy/qgpo	python3 -u ding/example/qgpo.py
47	MBSAC(SAC+MVE+SVG)	policy/mbpolicy/mbsac	python3 -u pendulum_mbsac_mbpo_config.py \ python3 -u pendulum_mbsac_ddppo_config.py
48	STEVESAC(SAC+STEVE+SVG)	policy/mbpolicy/mbsac	python3 -u pendulum_stevesac_mbpo_config.py
49	MBPO	MBPO doc world_model/mbpo	python3 -u pendulum_sac_mbpo_config.py
50	DDPPO	world_model/ddppo	python3 -u pendulum_mbsac_ddppo_config.py
51	DreamerV3	world_model/dreamerv3	python3 -u cartpole_balance_dreamer_config.py
52	PER	worker/replay_buffer	`rainbow demo`
53	GAE	rl_utils/gae	`ppo demo`
54	ST-DIM	torch_utils/loss/contrastive_loss	ding -m serial -c cartpole_dqn_stdim_config.py -s 0
55	PLR	PLR doc data/level_replay/level_sampler	python3 -u bigfish_plr_config.py -s 0
56	PCGrad	torch_utils/optimizer_helper/PCGrad	python3 -u multi_mnist_pcgrad_main.py -s 0

Environment Versatility

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No	Environment	Code and Doc Links
1	Atari	dizoo link env tutorial 环境指南
2	box2d/bipedalwalker	dizoo link env tutorial 环境指南
3	box2d/lunarlander	dizoo link env tutorial 环境指南
4	classic_control/cartpole	dizoo link env tutorial 环境指南
5	classic_control/pendulum	dizoo link env tutorial 环境指南
6	competitive_rl	dizoo link 环境指南
7	gfootball	dizoo link env tutorial 环境指南
8	minigrid	dizoo link env tutorial 环境指南
9	MuJoCo	dizoo link env tutorial 环境指南
10	PettingZoo	dizoo link env tutorial 环境指南
11	overcooked	dizoo link env tutorial
12	procgen	dizoo link env tutorial 环境指南
13	pybullet	dizoo link 环境指南
14	smac	dizoo link env tutorial 环境指南
15	d4rl	dizoo link 环境指南
16	league_demo	dizoo link
17	pomdp atari	dizoo link
18	bsuite	dizoo link env tutorial 环境指南
19	ImageNet	dizoo link 环境指南
20	slime_volleyball	dizoo link env tutorial 环境指南
21	gym_hybrid	dizoo link env tutorial 环境指南
22	GoBigger	dizoo link env tutorial 环境指南
23	gym_soccer	dizoo link 环境指南
24	multiagent_mujoco	dizoo link 环境指南
25	bitflip	dizoo link 环境指南
26	sokoban	dizoo link env tutorial 环境指南
27	gym_anytrading	dizoo link env tutorial
28	mario	dizoo link env tutorial 环境指南
29	dmc2gym	dizoo link env tutorial 环境指南
30	evogym	dizoo link env tutorial 环境指南
31	gym-pybullet-drones	dizoo link 环境指南
32	beergame	dizoo link 环境指南
33	classic_control/acrobot	dizoo link 环境指南
34	box2d/car_racing	dizoo link 环境指南
35	metadrive	dizoo link 环境指南
36	cliffwalking	dizoo link env tutorial 环境指南
37	tabmwp	dizoo link env tutorial 环境指南
38	frozen_lake	dizoo link env tutorial 环境指南
39	ising_model	dizoo link env tutorial 环境指南

means discrete action space

means continuous action space

means hybrid (discrete + continuous) action space

means multi-agent RL environment

means environment which is related to exploration and sparse reward

means offline RL environment

means Imitation Learning or Supervised Learning Dataset

means environment that allows agent VS agent battle

P.S. some enviroments in Atari, such as MontezumaRevenge, are also the sparse reward type.

General Data Container: TreeTensor

DI-engine utilizes TreeTensor as the basic data container in various components, which is ease of use and consistent across different code modules such as environment definition, data processing and DRL optimization. Here are some concrete code examples:

TreeTensor can easily extend all the operations of torch.Tensor to nested data:

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import treetensor.torch as ttorch


# create random tensor
data = ttorch.randn({'a': (3, 2), 'b': {'c': (3, )}})
# clone+detach tensor
data_clone = data.clone().detach()
# access tree structure like attribute
a = data.a
c = data.b.c
# stack/cat/split
stacked_data = ttorch.stack([data, data_clone], 0)
cat_data = ttorch.cat([data, data_clone], 0)
data, data_clone = ttorch.split(stacked_data, 1)
# reshape
data = data.unsqueeze(-1)
data = data.squeeze(-1)
flatten_data = data.view(-1)
# indexing
data_0 = data[0]
data_1to2 = data[1:2]
# execute math calculations
data = data.sin()
data.b.c.cos_().clamp_(-1, 1)
data += data ** 2
# backward
data.requires_grad_(True)
loss = data.arctan().mean()
loss.backward()
# print shape
print(data.shape)
# result
# <Size 0x7fbd3346ddc0>
# ├── 'a' --> torch.Size([1, 3, 2])
# └── 'b' --> <Size 0x7fbd3346dd00>
#     └── 'c' --> torch.Size([1, 3])

TreeTensor can make it simple yet effective to implement classic deep reinforcement learning pipeline

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import torch
import treetensor.torch as ttorch

B = 4


def get_item():
    return {
        'obs': {
            'scalar': torch.randn(12),
            'image': torch.randn(3, 32, 32),
        },
        'action': torch.randint(0, 10, size=(1,)),
        'reward': torch.rand(1),
        'done': False,
    }


data = [get_item() for _ in range(B)]


# execute `stack` op
- def stack(data, dim):
-     elem = data[0]
-     if isinstance(elem, torch.Tensor):
-         return torch.stack(data, dim)
-     elif isinstance(elem, dict):
-         return {k: stack([item[k] for item in data], dim) for k in elem.keys()}
-     elif isinstance(elem, bool):
-         return torch.BoolTensor(data)
-     else:
-         raise TypeError("not support elem type: {}".format(type(elem)))
- stacked_data = stack(data, dim=0)
+ data = [ttorch.tensor(d) for d in data]
+ stacked_data = ttorch.stack(data, dim=0)

# validate
- assert stacked_data['obs']['image'].shape == (B, 3, 32, 32)
- assert stacked_data['action'].shape == (B, 1)
- assert stacked_data['reward'].shape == (B, 1)
- assert stacked_data['done'].shape == (B,)
- assert stacked_data['done'].dtype == torch.bool
+ assert stacked_data.obs.image.shape == (B, 3, 32, 32)
+ assert stacked_data.action.shape == (B, 1)
+ assert stacked_data.reward.shape == (B, 1)
+ assert stacked_data.done.shape == (B,)
+ assert stacked_data.done.dtype == torch.bool

Feedback and Contribution

File an issue on Github
Open or participate in our forum
Discuss on DI-engine discord server
Discuss on DI-engine slack communication channel
Discuss on DI-engine's WeChat group (i.e. add us on WeChat: ding314assist)
Contact our email ([email protected])
Contributes to our future plan Roadmap

We appreciate all the feedbacks and contributions to improve DI-engine, both algorithms and system designs. And CONTRIBUTING.md offers some necessary information.

Supporters

↳ Stargazers

↳ Forkers

Citation

@misc{ding,
    title={DI-engine: OpenDILab Decision Intelligence Engine},
    author={OpenDILab Contributors},
    publisher={GitHub},
    howpublished={\url{https://github.com/opendilab/DI-engine}},
    year={2021},
}

License

DI-engine released under the Apache 2.0 license.

opendilab / di-engine Goto Github PK

di-engine's Introduction

Introduction to DI-engine

Outline

Installation

Quick Start

Feature

Algorithm Versatility

Environment Versatility

General Data Container: TreeTensor

Feedback and Contribution

Supporters

↳ Stargazers

↳ Forkers

Citation

License

di-engine's People

Contributors

Stargazers

Watchers

Forkers

di-engine's Issues

Overview

TODO

Issue Description

Steps to Reproduce

What Do We Need?

What do I find?

Why this is not okay?

How to solve this problem

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