Noise parameters in Reinforcement learning DDPG
조회 수: 95(최근 30일)
표시 이전 댓글
Surya teja Tunuguntla 2019년 6월 14일
댓글: Rajesh Siraskar 2019년 12월 10일
What should be the values of Noise parameters (for agent) if my action range is between -0.5 to -5 in DDPG reinforcement learning I want to explore whole action range for each sample time? Also is there anyway to make the noise options (for agent) independent of sample time?
댓글 수: 0
Drew Davis 2019년 6월 19일
편집: Drew Davis 2019년 6월 19일
It is fairly common to have Variance*sqrt(SampleTime) somewhere between 1 and 10% of your action range for Ornstein Uhlenbeck (OU) action noise. So in your case, the variance can be set between 4.5*0.01/sqrt(SampleTime) and 4.5*0.10/sqrt(SampleTime). The other important factor is the VarianceDecayRate, which will dictate how fast the variance will decay. You can calculate how many samples it will take for your variance to be halved by this simple formula:
halflife = log(0.5)/log(1-VarianceDecayRate)
It is critically important for your agent to explore while learning so keeping the VarianceDecayRate small (or even zero) is a good idea. The other noise parameters can usually be left as default.
You can check out this pendulum example which does a pretty good job of exploring during training.
The sample time of the noise options will be inherited by the agent, so it is not necessary to configure. By default, the noise model will be queried at the same rate as the agent.
Hope this helps
댓글 수: 5
Find more on Reinforcement Learning in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!Start Hunting!