This is the implementation of the paper “Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning”.
For more paper information, please checkout the the paper Link.
The class PID algorithms has no training process.
python run.py --choose_model class_pid --curve_type trapezoidal --height 1000 --run_type test
Plot the result:
python plot_result.py --choose_model class_pid --curve_type trapezoidal --height 1000 --run_type test
Search the parameter of PID Based DDPG Algorithm.
python run.py --choose_model search_pid_parameter --curve_type trapezoidal --height 1000 --run_type train
experimental operation process:
python run.py --choose_model search_electric --curve_type trapezoidal --height 1000 --run_type train
test
python run.py --choose_model search_electric --curve_type trapezoidal --height 1000 --run_type test
Plot the result:
python plot_result.py --choose_model search_electric --curve_type trapezoidal --height 1000 --run_type train
the result show:
The code was tested under Ubuntu 16 and uses these packages:
- tensorflow-gpu==1.14.0
- atari-py==0.2.6
- gym==0.17.3
- numpy==1.91.3
more packages described in requirements.txt
If you find this open source release useful, please reference in your paper:
Chen P, He Z, Chen C, et al. (2018). Control strategy of speed servo systems based on deep reinforcement learning[J]. Algorithms, 2018, 11(5): 65..