README.md

metadata

env_name: MountainCar-v0
tags:
  - MountainCar-v0
  - reinforce_rnd
  - reinforcement-learning
  - custom-implementation
  - curiosity
  - reinforce
  - rnd
  - policy-gradient
  - pytorch
model-index:
  - name: Curiosity-MountainCarV0
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: MountainCar-v0
          type: MountainCar-v0
        metrics:
          - type: mean_reward
            value: '-114.75 +/- 11.42'
            name: mean_reward
            verified: false

Reinforce_RND Agent playing MountainCar-v0

This is a trained model of a Reinforce_RND agent playing MountainCar-v0.

Usage

create the conda env in https://github.com/GeneHit/drl_practice

conda create -n drl python=3.10
conda activate drl
python -m pip install -r requirements.txt

play with full model

# load the full model
model = load_from_hub(repo_id="winkin119/Curiosity-MountainCarV0", filename="full_model.pt")

# Create the environment. 
env = gym.make("MountainCar-v0")
state, _ = env.reset()
action = model.action(state)
...

There is also a state dict version of the model, you can check the corresponding chapter in the repo.