# reinforcement learning

### What is Acceptably Safe for Reinforcement Learning?

**Learning**algorithms are becoming more prevalent in critical systems where dynamic decision making and efficiency are the ...of

**Reinforcement**

**Learning**in particu- lar, considering the ...

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### Exploring Deep Reinforcement Learning with Multi Q Learning

**reinforcement**

**learning**algorithm which often explicitly stores state values using lookup ...Q-

**learning**, achieving average returns up to ...

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### Deep Reinforcement Learning for Swarm Systems

**reinforcement**

**learning**for swarms: the high and possibly changing dimensionality of information perceived by each ...that

**learning**embeddings end-to-end using neural network features scales well ...

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### Paraphrase Generation with Deep Reinforcement Learning

**learning**for paraphrasing ...Seq2Seq

**learning**model with attention and copy mecha- nism (Bahdanau et ...inverse

**reinforcement**

**learning**(IRL) with outputs from the generator as supervisions ...

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### Applications of deep learning and reinforcement learning to biological data

**learning**techniques. Overall, recent research in Deep

**learning**(DL),

**Reinforcement**

**learning**(RL), and their combination (Deep RL) promise to revolutionize Artificial ...

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### Sentence Simplification with Deep Reinforcement Learning

**reinforcement**

**learning**framework (Williams, 1992): it explores the space of possible simplifications while learn- ing to maximize an expected reward function that encourages outputs which meet ...

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### Practical Kernel-Based Reinforcement Learning

**reinforcement**

**learning**(Sch¨ olkopf and Smola, ...of

**reinforcement**

**learning**is to “kernelize” some formulation of the value-function approximation problem (Xu et ...to

**reinforcement**...

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### Deep Reinforcement Learning for Dialogue Generation

**reinforcement**

**learning**, which have been widely ap- plied in MDP and POMDP dialogue systems (see Re- lated Work section for ...ral

**reinforcement**

**learning**(RL) generation method, which can ...

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### Learning to Teach in Cooperative Multiagent Reinforcement Learning

**reinforcement**

**learning**(Ng and Russell 2000), apprenticeship

**learning**(Abbeel and Ng 2004), and

**learning**from demonstration (Argall et ...curriculum

**learning**(Bengio et ...

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### Reinforcement Learning for Generative Art

**reinforcement**

**learning**for creatives. RL5 allows ...

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### Collaborative reinforcement learning of autonomic behaviour

**Reinforcement**

**Learning**(CRL) is a bottom-up approach to tackling the complex time- varying problems of engineering autonomic behaviour for distributed systems where there is no support for ...

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### Neural Logic Reinforcement Learning

**reinforcement**

**learning**(DRL) has achieved significant breakthroughs in various ...Logic

**Reinforcement**

**Learning**(NLRL) to represent the policies in

**reinforcement**

**learning**by ...

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### Evolutionary Function Approximation for Reinforcement Learning

**reinforcement**

**learning**problems. In most real-world

**reinforcement**

**learning**tasks, TD methods require a function approximator to represent the value ...standard

**reinforcement**...

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### Lyapunov Design for Safe Reinforcement Learning

**reinforcement**

**learning**agents based on Lyapunov design ...any

**reinforcement**

**learning**algorithm and at all times, including while the agent is

**learning**and taking exploratory ...

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### Active Bayesian perception and reinforcement learning

**learning**is tested with a simple but illustrative task of perceiving object curvature using tapping movements of a biomimetic fingertip with unknown contact location ...standard

**reinforcement**...

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### Reinforcement Learning with Factored States and Actions

**learning**“macro” or “basis” ...during

**reinforcement**

**learning**, we find a set that can form useful actions, while excluding action combinations that are either not seen or not ...

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### Determinantal Reinforcement Learning

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### Hierarchical Average Reward Reinforcement Learning

**Reinforcement**

**learning**(RL) is a machine

**learning**framework for solving sequential decision- making problems. Despite its successes in a number of different domains, including backgammon (Tesauro, ...

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### Self reflective deep reinforcement learning

**reinforcement**

**learning**agents is difficult to train. It takes long to train because the agent does not have direct answer to the input in hand, it has to rely on own assessment of how good or bad ...

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### Construction of Approximation Spaces for Reinforcement Learning

**Reinforcement**

**learning**(RL, Sutton and Barto, 1998; Bertsekas and Tsitsiklis, 1996) provides a framework to autonomously learn control policies in stochastic environments and has become pop- ular in recent ...

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