> ML_LITERATURE // FUJIMOTO-2018-ADDRESSING-FUNCTION-APPROXIMATION-ERROR-ACTOR-CRITIC-TD3_v1.0
Addressing Function Approximation Error in Actor-Critic Methods (TD3)
Scott Fujimoto, Herke van Hoof, David Meger · International Conference on Machine Learning (ICML) (2018)
algorithm2018foundationalthirdPartyReproduced
Principal Contribution
Introduced Twin Delayed DDPG (TD3), incorporating clipped double Q-learning, delayed policy updates, and target policy smoothing to eliminate actor-critic overestimation.
Operational Relevance
Serves as qualified theoretical and systems foundation for task-reinforcement-learning, task-continuous-control.
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: