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Found 9 papers, 2 papers with code
Batch Reinforcement Learning with Hyperparameter Gradients
We consider the batch reinforcement learning problem where the agent needs to learn only from a fixed batch of data, without further interaction with the environment.
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
This paves the way for new research directions, e. g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.
Model-based Reinforcement Learning
reinforcement-learning
+2
Policy Optimization Through Approximate Importance Sampling
These proxy objectives allow stable and low variance policy learning, but require small policy updates to ensure that the proxy objective remains an accurate approximation of the target policy value.
Distributed Multitask Reinforcement Learning with Quadratic Convergence
Multitask reinforcement learning (MTRL) suffers from scalability issues when the number of tasks or trajectories grows large.
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
The models are trained on dialogues generated by a simulated user and the best model is then used to train a policy on-line which is shown to perform at least as well as a baseline system using prior knowledge of the user's task.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on.
Cost-Sensitive Exploration in Bayesian Reinforcement Learning
In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected long-term total reward.
