Pogo-Stick Model
This system is used to research the usefulness of reinforcement learning for defining power conservative control strategies for flexible-legged jumping systems. The details on this system can be found (enter publication link).
Either:
- See
Concurrent Design/gym_pogo_stick/README.md- FIXME: Needs updating per concurrent design work
- See
Learn Control/gym_pogo_stick/README.md - See
Learn Mechanical Parameters/pogo_stick_jumping/README.md- FIXME: DOES NOT EXIT
| Parameter | Description |
|---|---|
numJumps |
2, int: number of jumps the pogo completes to terminate an episode |
linear |
"Linear", string: type of spring used in environment. "Nonlinear" |
trainRobust |
False, bool: whether or not the env parameters change during training |
epSteps |
500, int: number of steps to terminate an episode |
evaluating |
False, bool: whether or not the position of the actuator is randomly set at reset |
rewardType |
"Height", string: what the agent is going to learn to accomplish. "Efficiency" "SpecHei" "SpHeEf" |
specifiedHeight |
0.05, float: the height the agent learns to jump to for rewardType "SpecHei" and "SpHeEf" |
captureData |
False, bool: whether or not to capture the time series data for environment [Time, Reward, Input, RodPos, RodVel, ActPos, ActVel] |
saveDataName |
None, string: name of the data when captured, default is "Data_xxx_xxx" where xxx_xxx are date and time stamps |
saveDataLocation |
None, string or Path: location to save the data, default is "Captured_Data" within current working directory |
See random_action.py for example use.