  Developing an agent in reinforcement learning (RL) that is capable of
performing complex control tasks directly from high-dimensional observation
such as raw pixels is yet a challenge as efforts are made towards improving
sample efficiency and generalization. This paper considers a learning framework
for Curiosity Contrastive Forward Dynamics Model (CCFDM) in achieving a more
sample-efficient RL based directly on raw pixels. CCFDM incorporates a forward
dynamics model (FDM) and performs contrastive learning to train its deep
convolutional neural network-based image encoder (IE) to extract conducive
spatial and temporal information for achieving a more sample efficiency for RL.
In addition, during training, CCFDM provides intrinsic rewards, produced based
on FDM prediction error, encourages the curiosity of the RL agent to improve
exploration. The diverge and less-repetitive observations provide by both our
exploration strategy and data augmentation available in contrastive learning
improve not only the sample efficiency but also the generalization. Performance
of existing model-free RL methods such as Soft Actor-Critic built on top of
CCFDM outperforms prior state-of-the-art pixel-based RL methods on the DeepMind
Control Suite benchmark.
