README.md

metadata

library_name: stable-baselines3
tags:
  - PandaReachDense-v3
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: A2C
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: PandaReachDense-v3
          type: PandaReachDense-v3
        metrics:
          - type: mean_reward
            value: '-0.17 +/- 0.10'
            name: mean_reward
            verified: false

A2C Agent playing PandaReachDense-v3

This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library.

Usage (with Stable-baselines3)

TODO: Add your code

from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize

# Load the saved statistics
eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)

# We need to override the render_mode
eval_env.render_mode = "rgb_array"

#  do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False

# Load the agent
model = A2C.load("a2c-PandaReachDense-v3")

mean_reward, std_reward = evaluate_policy(model, eval_env)

print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

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