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Deep Learning: Difference between revisions
→Actor-critic algorithms
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<math>\nabla_{\theta} J(\theta) \approx \frac{1}{N} \sum_{i=1}^{N} \sum_{t=1}^{T} \nabla_{\theta} \log \pi_{\theta}(a_t^{(i)} | s_t^{(i)}) \left(Q(s_t^{(i)}, a_t^{(i)} - V(s_t^{(i)})\right)</math> | <math>\nabla_{\theta} J(\theta) \approx \frac{1}{N} \sum_{i=1}^{N} \sum_{t=1}^{T} \nabla_{\theta} \log \pi_{\theta}(a_t^{(i)} | s_t^{(i)}) \left(Q(s_t^{(i)}, a_t^{(i)} - V(s_t^{(i)})\right)</math> | ||
===Other topics in RL=== | |||
* Inverse RL | |||
* Multi-agent RL | |||
* Model-based RL | |||
==Summary of Course== | |||
;What we covered | |||
* Supervised DL | |||
* Unsupervised DL (GANs, VAEs) | |||
* Self-supervised DL | |||
* Meta-Learning | |||
* Learning with Attention (Transformers) | |||
* Deep RL | |||
* Optimization | |||
* Generalization | |||
* Robustness | |||
* Interpretability | |||
;What we didn't cover | |||
* Fairness | |||
* Privacy & Ethics | |||
* Bayesian DL | |||
* Federated Learning | |||
* Graph NNs | |||
;Things which may be on the final | |||
* Transformers | |||
* Wasserstein distance | |||
* Kernel methods | |||
==Misc== | ==Misc== | ||