[PDF] Top 20 Transfer in Deep Reinforcement Learning Using Knowledge Graphs

... the Knowledge Graph DQN (KG-DQN) and touched on some aspects of transfer learning, show- ing that pre-training portions of the deep Q- network using question answering system on per- ... See full document

... Figure 12 is the schematic diagram of ‘S’ ramp. The intelligent vehicle is initially situated in the left lane of lower right corner. After driving at a yaw angle from the north, the vehicle turns left and enters ... See full document

... strategies using convolu- tional neural networks, trained with a variant of Q-learning (Watkins and Dayan, ...use deep reinforcement learning for training, our work has important ... See full document

... novel deep reinforcement learn- ing framework for incentivizing users to rebalance such sys- ...novel deep reinforcement learning al- gorithm called Hierarchical Reinforcement ... See full document

... Active learning aims to select a small sub- set of data for annotation such that a classi- fier learned on the data is highly ...done using heuristic selec- tion methods, however the effectiveness of such ... See full document

... Overall, the low F-measures demonstrate the difficulty of the task, as they are consistently low for all methods. We use exact sentences from a clinical narrative as queries to search for the diag- noses in the ... See full document

... Transfer learning in RL is an important topic to address at this time for three ...machine learning techniques are either unable or ill-equipped to address ...machine learning techniques such ... See full document

... combining Reinforcement Learning with neural networks, TD- Gammon [10] showed that an agent could learn how to play the board game Backgam- mon by playing against itself and learning from the results ... See full document

... unfamiliar player populations. A narrative planning policy’s generalizability is essential to consider because the deployment environment of an interactive narrative planner is often challenging to control, and it can ... See full document

... of deep learning and RL, and by observing just the screen pixels, deep RL computer programs have beaten a world champion in the game Go and played many Atari 2600 video games better than humans [5] ... See full document

... Results are shown in Figure 6b,c, for modeling last and model best, respectively, with a dot per episode. Green dots show successful episodes and red dots unsuccessful ones. The y-value is the final reward of the ... See full document

... Inverse reinforcement learning (IRL) aims to learn a reward function from expert demonstra- ...ship learning, which uses a feature based linear reward function and learns to match feature ex- ... See full document

... study deep reinforcement algorithms for partially observable Markov decision processes (POMDP) combined with Deep ...our knowledge, we are the first to apply standard Markov decision process ... See full document

... proposed learning method is generic and doesn’t require any prior knowledge of the ...agent using a determin- istic optimal ...A deep convolutional neural network is trained on the captured ... See full document

... The reinforcement learning (RL) community has made great strides in designing algorithms capable of exceeding human performance on specific ...the learning algo- rithm is general, but each solution ... See full document

... Figure 2: Total SR and SR-10 for two relations of NELL-995. DeepPath / AttnPath TransD means using TransD as the pretrained embeddings. AttnPath MS/RR is adding MSR and MRR to fine-tune hyperparameters. AttnPath ... See full document

... multi-task learning can be achieved by concatenating the output for each task and learning a single neural network that simultaneously classifies all ...when learning new ... See full document

... with deep learning ...with deep reinforcement ...know”. Knowledge in Brain Science and Cognitive Psychology, such as emotion, consciousness and other characters of sociology, psychology ... See full document

... for transfer that incorporates the transfer knowledge as bias transfer function values in the initial action state value ...Bias transfer method that does not a ff ect the convergence of ... See full document

... off-policy reinforcement-learning methods to reuse past ...of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the expe- riences ... See full document