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TA_RIAC_SAC_bipedal_s3

Reinforcement Learning
sac
deep-reinforcement-learning
teach-my-agent-parkour
Eval Results
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TA_RIAC_SAC_bipedal_s3 / README.md
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metadata
tags:
  - sac
  - deep-reinforcement-learning
  - reinforcement-learning
  - teach-my-agent-parkour
model-index:
  - name: RIAC_SAC_bipedal_s3
    results:
      - metrics:
          - type: mean_reward
            value: 190.29 +/- 143.70
            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': 'old_classic_bipedal'}