deep reinforcement learning
papers
- Asynchronous Methods for Deep Reinforcement Learning. [arxiv] :star:
- Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR. [arxiv]
- A New Softmax Operator for Reinforcement Learning.[url]
- Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., ICML. [arxiv]
- Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR. [arxiv]
- Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR. [arxiv]
- Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv. [url]
- Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML. [arxiv]
- Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., ICML. [arxiv]
- Continuous control with deep reinforcement learning. [arxiv] :star:
- Deep Successor Reinforcement Learning. [arxiv]
- Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop. [arxiv]
- Deep Exploration via Bootstrapped DQN. [arxiv] :star:
- Deep Reinforcement Learning for Dialogue Generation. [arxiv]
tensorflow
- Deep Reinforcement Learning in Parameterized Action Space. [arxiv] :star:
- Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments.[url]
- Designing Neural Network Architectures using Reinforcement Learning.
arxiv code
- Dialogue manager domain adaptation using Gaussian process reinforcement learning. [arxiv]
- End-to-End Reinforcement Learning of Dialogue Agents for Information Access. [arxiv]
- Generating Text with Deep Reinforcement Learning. [arxiv]
- Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv. [arxiv]
- Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv. [arxiv]
- Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv. [arxiv]
- Hierarchical Object Detection with Deep Reinforcement Learning. [arxiv]
- High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR. [arxiv]
- Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI. [arxiv]
- Interactive Spoken Content Retrieval by Deep Reinforcement Learning. [arxiv]
- Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv. [url]
- Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv. [url]
- Learning to compose words into sentences with reinforcement learning. [url]
- Loss is its own Reward: Self-Supervision for Reinforcement Learning.[arxiv]
- Model-Free Episodic Control. [arxiv]
- Mastering the game of Go with deep neural networks and tree search. [nature] :star:
- MazeBase: A Sandbox for Learning from Games .[arxiv]
- Neural Architecture Search with Reinforcement Learning. [pdf]
- Neural Combinatorial Optimization with Reinforcement Learning. [arxiv]
- Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning. [url]
- Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation. arXiv. [arxiv]
- Policy Distillation, A. A. Rusu et at., ICLR. [arxiv]
- Prioritized Experience Replay. [arxiv] :star:
- Reinforcement Learning Using Quantum Boltzmann Machines. [arxiv]
- Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al.[arxiv]
- Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving. [arxiv]
- Sample-efficient Deep Reinforcement Learning for Dialog Control. [url]
- Self-Correcting Models for Model-Based Reinforcement Learning.[url]
- Unifying Count-Based Exploration and Intrinsic Motivation. [arxiv]
- Value Iteration Networks. [arxiv]