> ML_LITERATURE // HESSEL-2018-RAINBOW-COMBINING-IMPROVEMENTS-IN-DEEP-RL_v1.0
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver · AAAI Conference on Artificial Intelligence (2018)
seminal-architecture2018foundationalthirdPartyReproduced
Principal Contribution
Combined six independent extensions to DQN (Double Q, Prioritized Replay, Dueling Networks, Multi-step Learning, Distributional RL, and Noisy Nets) into a single unified agent.
Operational Relevance
Serves as qualified theoretical and systems foundation for task-reinforcement-learning.
Assumptions
- Markovian state dynamics and stationary reward functions hold in target evaluation environments
Limitations
- Sample efficiency, exploration stability, and real-world sim-to-real transfer gaps require specialized tuning