We lifted 4 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| MiuLab/D3Q | canonical | 1 of 1 |
| Yuqing2018/D3Q_Python3 | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| text_to_dict | Ran | MiuLab/D3Q/D3Q/src/deep_dialog/dialog_system/dict_reader.py code served (permissive licence) · get_code("5e4fd7cf91f440fc") |
| draw | Not yet run | Yuqing2018/D3Q_Python3/D3Q/src/draw_figure.py code served (permissive licence) · get_code("a97a8e0856ccb2bc") |
| read_performance | Not yet run | Yuqing2018/D3Q_Python3/D3Q/src/draw_figure.py code served (permissive licence) · get_code("49f5c7b0ffd86759") |
| show_model_performance | Not yet run | Yuqing2018/D3Q_Python3/D3Q/src/draw_figure.py code served (permissive licence) · get_code("8a8a9a5bf7ce2f6d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ's high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent's capability of adapting to a changing environment is tested.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1808.09442")
get_code_for_paper("1808.09442")
have("1808.09442")
Connect an agent — have() is free.