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https://github.com/ahn-github/tensorlayer/tree/wrapper/examples/reinforcement_learning#table-of-contents
https://github.com/ahn-github/tensorlayer/tree/wrapper/examples/reinforcement_learning#value-based
Technical note: Q-learning. Watkins et al. 1992http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf
Human-level control through deep reinforcement learning, Mnih et al. 2015.https://www.nature.com/articles/nature14236/
Schaul et al. Prioritized experience replay. Schaul et al. 2015.https://arxiv.org/abs/1511.05952
Dueling network architectures for deep reinforcement learning. Wang et al. 2015.https://arxiv.org/abs/1511.06581
Deep reinforcement learning with double q-learning. Van et al. 2016.https://arxiv.org/abs/1509.06461
Safe and efficient off-policy reinforcement learning. Munos et al. 2016: https://arxiv.org/pdf/1606.02647.pdf
Noisy networks for exploration. Fortunato et al. 2017.https://arxiv.org/pdf/1706.10295.pdf
A distributional perspective on reinforcement learning. Bellemare et al. 2017.https://arxiv.org/pdf/1707.06887.pdf
Reinforcement learning: An introduction. Sutton et al. 2011.https://www.cambridge.org/core/journals/robotica/article/robot-learning-edited-by-jonathan-h-connell-and-sridhar-mahadevan-kluwer-boston-19931997-xii240-pp-isbn-0792393651-hardback-21800-guilders-12000-8995/737FD21CA908246DF17779E9C20B6DF6
Abbeel et al. Trust region policy optimization. Schulman et al.2015.https://arxiv.org/pdf/1502.05477.pdf
Proximal policy optimization algorithms. Schulman et al. 2017.https://arxiv.org/abs/1707.06347
Emergence of locomotion behaviours in rich environments. Heess et al. 2017.https://arxiv.org/abs/1707.02286
Actor-critic algorithms. Konda er al. 2000.https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf
Asynchronous methods for deep reinforcement learning. Mnih et al. 2016.https://arxiv.org/pdf/1602.01783.pdf
Continuous Control With Deep Reinforcement Learning, Lillicrap et al. 2016https://arxiv.org/pdf/1509.02971.pdf
Addressing function approximation error in actor-critic methods. Fujimoto et al. 2018.https://arxiv.org/pdf/1802.09477.pdf
Soft actor-critic algorithms and applications. Haarnoja et al. 2018.https://arxiv.org/abs/1812.05905
https://github.com/ahn-github/tensorlayer/tree/wrapper/examples/reinforcement_learning#examples-of-rl-algorithms
Technical Note Q-Learninghttp://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf
Human-level control through deep reinforcementlearninghttps://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
Playing Atari with Deep Reinforcement Learninghttps://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
Deep Reinforcement Learning with Double Q-learninghttps://arxiv.org/abs/1509.06461
Prioritized Experience Replayhttps://arxiv.org/abs/1511.05952
A Distributional Perspective on Reinforcement Learninghttps://arxiv.org/pdf/1707.06887.pdf
Safe and Efficient Off-Policy Reinforcement Learninghttps://arxiv.org/abs/1606.02647
Actor-Critic Algorithmshttps://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf
Asynchronous Methods for Deep Reinforcement Learninghttps://arxiv.org/pdf/1602.01783.pdf
Soft Actor-Critic Algorithms and Applicationshttps://arxiv.org/pdf/1812.05905.pdf
Policy Gradient Methods for Reinforcement Learning with Function Approximationhttps://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf
Continuous Control With Deep Reinforcement Learninghttps://arxiv.org/pdf/1509.02971.pdf
Addressing Function Approximation Error in Actor-Critic Methodshttps://arxiv.org/pdf/1802.09477.pdf
Trust Region Policy Optimizationhttps://arxiv.org/pdf/1502.05477.pdf
Proximal Policy Optimization Algorithmshttps://arxiv.org/pdf/1707.06347.pdf
Emergence of Locomotion Behaviours in Rich Environmentshttps://arxiv.org/pdf/1707.02286.pdf
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