  Recent advances in off-policy deep reinforcement learning (RL) have led to
impressive success in complex tasks from visual observations. Experience replay
improves sample-efficiency by reusing experiences from the past, and
convolutional neural networks (CNNs) process high-dimensional inputs
effectively. However, such techniques demand high memory and computational
bandwidth. In this paper, we present Stored Embeddings for Efficient
Reinforcement Learning (SEER), a simple modification of existing off-policy RL
methods, to address these computational and memory requirements. To reduce the
computational overhead of gradient updates in CNNs, we freeze the lower layers
of CNN encoders early in training due to early convergence of their parameters.
Additionally, we reduce memory requirements by storing the low-dimensional
latent vectors for experience replay instead of high-dimensional images,
enabling an adaptive increase in the replay buffer capacity, a useful technique
in constrained-memory settings. In our experiments, we show that SEER does not
degrade the performance of RL agents while significantly saving computation and
memory across a diverse set of DeepMind Control environments and Atari games.
Finally, we show that SEER is useful for computation-efficient transfer
learning in RL because lower layers of CNNs extract generalizable features,
which can be used for different tasks and domains.
