README.md

metadata

library_name: stable-baselines3
tags:
  - PandaReachDense-v3
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: SAC
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: PandaReachDense-v3
          type: PandaReachDense-v3
        metrics:
          - type: mean_reward
            value: '-0.25 +/- 0.11'
            name: mean_reward
            verified: false

SAC Agent playing PandaReachDense-v3

This is a trained model of a SAC agent playing PandaReachDense-v3 using the stable-baselines3 library.

Usage (with Stable-baselines3)

Copy the code:

from stable_baselines3 import SAC

model = SAC("MultiInputPolicy", env, learning_rate = 0.00073, gamma = 0.98, gradient_steps = 64, verbose=1)
model.learn(5_000)