metadata
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: Setter-Solver_SAC_chimpanzee_s10
results:
- metrics:
- type: mean_reward
value: '-80.24 +/- 5.93'
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the Overall column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---|---|---|---|---|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | 42.7 (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | 26.4 (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | 37.4 (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | 1.0 (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
Hyperparameters
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'Setter-Solver'
'morphology': 'climbing_profile_chimpanzee'}