Comments (1)
Discussion
MountainCarContinuous
has some dependencies between time-series actions or states-actions.
- using HAC(Hierarchical Actor Critic), this gap can be handled because all states(position and velocity) could be subgoals.
- on the other hand, using UOF, the gap couldn't be handled because we cannot choose the optimal subgoals(we don't know exactly about the domain information of environments)
MountainCarContinuous
has just one final goal and a small range of start position.
- it makes UOF harder to converge. Since UOF just uses some subgoal(even we don't know exactly this goal is good or not), we cannot get various states and achieved goals for the hindsight framework.
MountainCarContinuous
requires harder and continuous hierarchical reasoning.
- In UOF paper, the algorithm was tested only block-stacking manipulation problem with various start positions and goal positions. In this environment, the agent's state is not affecting its behavior.
- We cannot handle
MountainCarContinuous
only using discrete (and unknown) subgoals
Closing this issue,
- we need to test on block-stacking manipulation problem.
- UOF is not good enough for general hierarchical framwork.
from project-sandwich-man.
Related Issues (16)
- [S-00-1] Project Planning HOT 6
- [S-02-1] Understanding HAC & UOF theory and sources HOT 1
- [S-02-3] HAC + SAC Implementation HOT 1
- [S-02-4] UOF + SAC Implementation HOT 1
- [S-02-5] Panda-gym + HAC Implementation HOT 1
- [S-02-6] Panda-gym + UOF Implementation
- [S-02-7] Comparison between HAC and UOF
- Extend HAC Experiment on panda-gym HOT 1
- [S-00-2] Problem Survey HOT 6
- Good aproach to goal generation
- [S-01] Single-step and Multi-goal tasks on Panda HOT 1
- [S1-01-1] PandaReach, PandaPickAndPlace, assign @benthebear93 HOT 2
- [S1-01-2] PandaPush, PandaStack, assign @CUN-bjy HOT 3
- make format exclude rule needed
- [S-02] Multi-step(hierarchical) and Multi-goal tasks on Panda
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