Beyond Tabula Rasa: Reincarnating Reinforcement Learning

while simultaneously using its environment interactions for RL. We start with a  (IQM) metric from the Benchmarking PVRL algorithms on Atari, with teacher-normalized scores aggregated across 10 games. Tabula rasa DQN (–·–) obtains a normalized score of 0.4. Standard baseline approaches include , and , a widely used deep RL benchmark. First, we take a optimizer and fine-tune it with the Reincarnating DQN (Adam) via Fine-Tuning. The vertical separator corresponds to loading network weights and replay data for fine-tuning. Left: Tabula rasa Nature DQN nearly converges in performance after 200M environment frames. Right: Fine-tuning this Nature DQN agent using a reduced learning rate with the Adam optimizer for 20 million frames obtains similar results to DQN (Adam) trained from scratch for 400M frames.

Given the DQN (Adam) agent as a starting point, fine-tuning is restricted to the 3-layer ) and a better neural network architecture (Reincarnating a different architecture / algorithm via QDagger. The vertical separator is the point at which we apply offline pre-training using QDagger for reincarnation. Left: Fine-tuning DQN with Adam. Right: Comparison of a tabula rasa Impala-CNN Rainbow agent (sky blue) to an Impala-CNN Rainbow agent (pink) trained using QDagger RRL from the fine-tuned DQN (Adam). The reincarnated Impala-CNN Rainbow agent consistently outperforms its scratch counterpart. Note that further fine-tuning DQN (Adam) results in diminishing returns (yellow).

Overall, these results indicate that past research could have been accelerated by incorporating a RRL approach to designing agents, instead of re-training agents from scratch. Our , where we demonstrate that RRL allows us to make progress on the problem of navigating stratospheric balloons using only a few hours of TPU-compute by reusing a and for a broader discussion on scientific comparisons, generalizability and reproducibility in RRL. Overall, we hope that this work motivates researchers to release computational work (e.g., model checkpoints) on which others could directly build. In this regard, we have open-sourced with their final replay buffers. We believe that reincarnating RL can substantially accelerate research progress by building on prior computational work, as opposed to always starting from scratch.

Acknowledgements

This work was done in collaboration with Pablo Samuel Castro, Aaron Courville and Marc Bellemare. We’d like to thank Tom Small for the animated figure used in this post. We are also grateful for feedback by the anonymous NeurIPS reviewers and several members of the Google Research team, DeepMind and Mila.