​​CURL: Contrastive Unsupervised Representations for Reinforcement Learning
This paper introduces a new method that significantly improves the sample efficiency of RL algorithms when learning from raw pixel data.
CURL architecture consists of three models: Query Encoder, Key Encoder, and RL agent. Query Encoder outputs embedding which used in RL agent as state representation. Contrastive loss computed from outputs of Query Encoder and Key Encoder. An important thing is that Query Encoder learns to minimize both RL and contrastive losses which allow all models to be trained jointly.
The method was tested on Atari and DeepMind Control tasks with limited interaction steps. It showed SOTA results for most of these tasks.
Paper: https://arxiv.org/abs/2004.04136.pdf
Code: https://github.com/MishaLaskin/curl
#rl #agent #reinforcement #learning
This paper introduces a new method that significantly improves the sample efficiency of RL algorithms when learning from raw pixel data.
CURL architecture consists of three models: Query Encoder, Key Encoder, and RL agent. Query Encoder outputs embedding which used in RL agent as state representation. Contrastive loss computed from outputs of Query Encoder and Key Encoder. An important thing is that Query Encoder learns to minimize both RL and contrastive losses which allow all models to be trained jointly.
The method was tested on Atari and DeepMind Control tasks with limited interaction steps. It showed SOTA results for most of these tasks.
Paper: https://arxiv.org/abs/2004.04136.pdf
Code: https://github.com/MishaLaskin/curl
#rl #agent #reinforcement #learning
