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We introduce Implicit Policy, a general class of expressive policies that can flexibly represent complex action distributions in reinforcement learning, with efficient algorithms to compute entropy regularized policy gradients.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 1937
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Dropout: A simple way to prevent neural networks from overfitting
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Policy gradient methods for reinforcement learning with function approximation
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Approximately optimal approximate reinforcement learning
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Apprenticeship learning via inverse reinforcement learning
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D. (2011) · 2011
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Degris, T., White, M., and Sutton, R. S. (2012) · 2012
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Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y. (2012) · 2012
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Kingma, D. P. and Welling, M. (2013) · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Deterministic policy gradient algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014) · 2014
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015) · 2015
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Pgq: Combining policy gradient and q-learning
O’Donoghue, B., Munos, R., Kavukcuoglu, K., and Mnih, V. (2016) · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · 2016
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An alternative softmax operator for reinforcement learning
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Openai baselines
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Noisy networks for exploration
Fortunato, M., Azar, M. G., Piot, B., Menick, J., Osband, I., Graves, A., Mnih, V., Munos, R., Hassabis, D., Pietquin, O., et al. (2017) · 2017
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016) · 2016
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Benchmarking deep reinforcement learning for continuous control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., and Abbeel, P. (2016) · 2016
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Finn, C., Christiano, P., Abbeel, P., and Levine, S. (2016) · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D. (2016) · 2016
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Latent space policies for hierarchical reinforcement learning
Haarnoja, T., Hartikainen, K., Abbeel, P., and Levine, S. (2018a)
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Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S. (2017) · 2017
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Gradient estimators for implicit models
Li, Y. and Turner, R. E. (2017) · 2017
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Bridging the gap between value and policy based reinforcement learning
Nachum, O., Norouzi, M., Xu, K., and Schuurmans, D. (2017) · 2017
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Hierarchical implicit models and likelihood-free variational inference
Tran, D., Ranganath, R., and Blei, D. M. (2017) · 2017
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Levine, S. (2018) · 2018
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Simple random search provides a competitive approach to reinforcement learning
Mania, H., Guy, A., and Recht, B. (2018) · 2018
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