gladisor/Reinforcement-Learning-Applied-To-Metamaterial-Design

Using deep reinforcement learning to design a broadband acoustic cloak. Created under the supervision of PhD. Feruza Amirkulova and PhD Peter Gerstoft. With the help of: Linwei Zhou, Peter Lai, and Amaris De La Rosa.

Reinforcement Learning Applied To Metamaterial Design

Our aim in this research is to use reinforcement learning to design a broadband acoustic cloak through inverse design. For more information on the project you can view our presentations:

• MLSE 2020 Submission

• Youtube video

Demo

These are examples of episodes of the trained DDPG (left) and DDQN (right) algorithms controling the positioning of 4 cylinder from a random configuration to one which produces low TSCS. Both algorithms were trained for 8000 episodes to minimize the root mean square (RMS) of TSCS across a range of wavenumbers from 0.45-0.35 ka.

Example usage

from tscsRL.environments.TSCSEnv import ContinuousTSCSEnv
from tscsRL.agents import ddpg

env = ContinuousTSCSEnv(
	nCyl=2,
	kMax=0.45,
	kMin=0.35,
	nFreq=11,
	stepSize=0.5)

params = ddpg.default_params()
params['save_every'] = 100
params['decay_timesteps'] = 100
params['num_episodes'] = 120
params['noise_scale'] = 1.1
params['save_data'] = False
params['use_wandb'] = True

name = 'test_ddpg'

agent = ddpg.DDPGAgent(
	env.observation_space, 
	env.action_space, 
	params, 
	name)

agent.learn(env)	

Diagrams of training loops

DDPG:

DDQN:

Credits

Images: Linwei Zhou

Inspiration for structuring agents: Ray