Contents in Appendices: In Appendix A, we describe each of the components in GA T A in detail

In Appendix A, we describe each of the components in GA T A in detail. In Appendix B, we provide detailed information on how we pre-train GA T A's graph updater GA T A. Since the action scorer module is the same as in GA T A, this appendix elaborates on In Appendix D, we provide additional results and discussions. In Appendix F, we show examples of graphs in TextWorld games. As briefly mentioned in Section 3.3, GA T A utilizes a graph encoder which is based on R-GCN [ The number of bases we use is 3. 14 A.2 T ext Encoder In the block, each convolutional layer has 64 filters, each kernel's size is 5. Following standard transformer training, we add positional encodings into each block's The representation aggregator aims to combine the text observation representations and graph representations together. The scorer consists of a self-attention layer, a masked mean pooling layer, and a two-layer MLP .