**Social-STGCNN** is a method for human trajectory prediction. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects.

**SHA-RNN**, or **Single Headed Attention RNN**, is a recurrent neural network, and language model when combined with an embedding input and [softmax](https://paperswithcode.com/method/softmax) classifier, based on a core [LSTM](https://paperswithcode.com/method/lstm) component and a [single-headed attention](https://paperswithcode.com/method/single-headed-attention) module. Other design choices include a Boom feedforward layer and the use of [layer normalization](https://paperswithcode.com/method/layer-normalization). The guiding principles of the author were to ensure simplicity in the architecture and to keep computational costs bounded (the model was originally trained with a single GPU).

SHA-RNN

Single Headed Attention RNN: Stop Thinking With Your Head

Social-STGCNN

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

**ACER**, or **Actor Critic with Experience Replay**, is an actor-critic deep reinforcement learning agent with [experience replay](https://paperswithcode.com/method/experience-replay). It can be seen as an off-policy extension of [A3C](https://paperswithcode.com/method/a3c), where the off-policy estimator is made feasible by:

- Using [Retrace](https://paperswithcode.com/method/retrace) Q-value estimation.
- Using truncated importance sampling with bias correction.
- Using a trust region policy optimization method.
- Using a [stochastic dueling network](https://paperswithcode.com/method/stochastic-dueling-network) architecture.

Source	Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com