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Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills.
Using the sir algorithm to simulate posterior distributions
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Information maximization in noisy channels: A variational approach
Barber, D. and Agakov, F. V · 2004
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Intrinsically motivated reinforcement learning
Chentanez, N., Barto, A. G., and Singh, S. P · 2005
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Entropies and cross-entropies of exponential families
Nielsen, F. and Nock, R · 2010
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Learning force control policies for compliant manipulation
Kalakrishnan, M., Righetti, L., Pastor, P., and Schaal, S · 2011
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Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots
Baranes, A. and Oudeyer, P.-Y · 2012
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Exploration in model-based reinforcement learning by empirically estimating learning progress
Lopes, M., Lang, T., Toussaint, M., and Oudeyer, P.-Y · 2012
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MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
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Variational information maximisation for intrinsically motivated reinforcement learning
Mohamed, S. and Rezende, D. J · 2015
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Universal Value Function Approximators
Schaul, T., Horgan, D., Gregor, K., and Silver, D · 2015
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Unifying count-based exploration and intrinsic motivation
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., and Munos, R · 2016
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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 · 2016
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Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Stadie, B. C., Levine, S., and Abbeel, P · 2016
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Hindsight Experience Replay
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., Mcgrew, B., Tobin, J., Abbeel, P., and Zaremba, W · 2017
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Path integral guided policy search
Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Schaal, S., and Levine, S · 2017
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Stochastic neural networks for hierarchical reinforcement learning
Florensa, C., Duan, Y., and Abbeel, P · 2017
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EX 2 : Exploration with Exemplar Models for Deep Reinforcement Learning
Fu, J., Co-Reyes, J. D., and Levine, S · 2017
Learning an Embedding Space for Transferable Robot Skills
Hausman, K., Springenberg, J. T., Wang, Z., Heess, N., and Riedmiller, M · 2018
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Data-Efficient Hierarchical Reinforcement Learning
Nachum, O., Brain, G., Gu, S., Lee, H., and Levine, S · 2018
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Visual Reinforcement Learning with Imagined Goals
Nair, A., Pong, V., Dalal, M., Bahl, S., Lin, S., and Levine, S · 2018
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Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Péré, A., Forestier, S., Sigaud, O., and Oudeyer, P.-Y · 2018
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Temporal Difference Models: Model-Free Deep RL For Model-Based Control
Pong, V., Gu, S., Dalal, M., and Levine, S · 2018
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β \beta -vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Count-based exploration with neural density models
Ostrovski, G., Bellemare, M. G., Oord, A., and Munos, R · 2017
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Curiosity-Driven Exploration by Self-Supervised Prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Tang, H., Houthooft, R., Foote, D., Stooke, A., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P · 2017
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Exploration by random network distillation
Burda, Y., Edwards, H., Storkey, A., and Klimov, O · 2018
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Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., and Meger, D · 2018
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Savinov, N., Raichuk, A., Marinier, R., Vincent, D., Pollefeys, M., Lillicrap, T., and Gelly, S · 2018
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Many-goals reinforcement learning
Veeriah, V., Oh, J., and Singh, S · 2018
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Unsupervised control through non-parametric discriminative rewards
Warde-Farley, D., de Wiele, T. V., Kulkarni, T., Ionescu, C., Hansen, S., and Mnih, V · 2018
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Large-scale study of curiosity-driven learning
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., and Efros, A. A · 2019
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Diversity is All You Need: Learning Skills without a Reward Function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2019
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Provably efficient maximum entropy exploration
Hazan, E., Kakade, S. M., Singh, K., and Soest, A. V · 2019
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Curiosity-driven experience prioritization via density estimation
Zhao, R. and Tresp, V · 2019
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Maximum entropy-regularized multi-goal reinforcement learning
Zhao, R., Sun, X., and Tresp, V · 2019
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