Curiosity-Driven Exploration - pytorch implementation w/ CartPole (Simple version) Paper: Curiosity-driven Exploration by Self-supervised Prediction, D. Pathak, 2017 Dependencies python 3.6.8 (Anaconda) pytorch 1.0.0 gym 0.12.1 Usage Easy easy. Install the dependencies and run the below command. python main.py Performance (sparse reward mode. only final penelty) Red: A2C with ICM, Blue: A2C w/o ICM A2C w/ ICM seems to converge slightly faster than the other on average in my experiments. Notes I trained the model in CartPole environment. However, it is not the best choice for experiment of curiosity I modified overall model architecture. A2C instead of A3C (Just Actor Critic using Advantage, not parallel technique). Very simple inverse model and forward model. Because the observation of CartPole is already some feature representations, not image. Larger scaling factor of intrinsic rewards. Other Codes Author's TensorFlow implementation with Doom
