Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown breakthrough success in natural language processing (NLP), achieving state-of-the-art results in domains such as language modeling and machine translation. Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting... (read more)

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Methods used in the Paper


METHOD TYPE
Cosine Annealing
Learning Rate Schedules
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
Variational Dropout
Regularization
Residual Connection
Skip Connections
Adaptive Input Representations
Input Embedding Factorization
Adaptive Softmax
Output Functions
Linear Warmup With Cosine Annealing
Learning Rate Schedules
Transformer-XL
Transformers
BPE
Subword Segmentation
Dense Connections
Feedforward Networks
Label Smoothing
Regularization
ReLU
Activation Functions
Adam
Stochastic Optimization
Softmax
Output Functions
Dropout
Regularization
Multi-Head Attention
Attention Modules
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
LSTM
Recurrent Neural Networks
Transformer
Transformers