README.md

metadata

tags:
  - sac
  - deep-reinforcement-learning
  - reinforcement-learning
  - teach-my-agent-parkour
model-index:
  - name: RIAC_SAC_fish_s2
    results:
      - metrics:
          - type: mean_reward
            value: 230.11 +/- 116.63
            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': 'RIAC'
'morphology': 'fish'}