> ML_LITERATURE // MNIH-2016-ASYNCHRONOUS-METHODS-FOR-DEEP-REINFORCEMENT-LEARNING-A3C_v1.0
Asynchronous Methods for Deep Reinforcement Learning (A3C)
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu · International Conference on Machine Learning (ICML) (2016)
algorithm2016foundationalthirdPartyReproduced
Principal Contribution
Replaced memory-heavy experience replay with multiple asynchronous parallel actor-learner threads running across CPU cores, decorrelating updates and drastically cutting training time.
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
Connected Algorithms, Architectures & Tools
Related Algorithms:
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Implementing Libraries: