Evaluating the Robustness of GAN-Based Inverse Reinforcement Learning Algorithms
We evaluate the robustness of reward functions learned with IRL, when transferred to similar tasks. We exceed state of the art results for one benchmark task and solve another one for the first time. Modifications are proposed that achieve faster and more stable training.
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/131625 |
| Acceso en línea: | https://hdl.handle.net/2117/131625 |
| Access Level: | acceso abierto |
| Palabra clave: | Reinforcement learning Algorithms inverse reinforcement learning IRL reinforcement learning RL guided cost learning GCL adversarial inverse reinforcement learning AIRL soft actor critic SAC transfer learning robustness maximum entropy principle maximum causal entropy principle reward shaping pre-training metric shaped reward loss pendulum lunar lander Aprenentatge per reforç Algorismes Àrees temàtiques de la UPC::Informàtica |
| Sumario: | We evaluate the robustness of reward functions learned with IRL, when transferred to similar tasks. We exceed state of the art results for one benchmark task and solve another one for the first time. Modifications are proposed that achieve faster and more stable training. |
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