Policy gradient reinforcement learning for fast quadrupedal locomotion

By Nate Kohl, Peter Stone. 2004

671 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Biomedical Engineering, Reinforcement Learning

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Citations in the corpus

End-to-End Training of Deep Visuomotor Policies (2015, background): “They have been applied to a range of robotic tasks, including manipulation [2, 13] and locomotion [5, 7, 15, 39].”
Learning Parameterized Skills (2012, methodology): “Direct policy search can be performed in three main ways: (1) by using black-box local search methods, such as hill climbing or the NelderMead simplex technique [34]; (2) by using gradient descent methods [86, 6, 72, 9]; or (3) by using EM or crossentropy approaches [31, 46].”
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation (2017, background): “[16, 21], and a range of other challenges such as helicopter flight [1].”
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates (2016, methodology): “Reinforcement learning (RL) methods have been applied to range of robotic control tasks, from locomotion [1], [2] to manipulation [3], [4], [5], [6] and autonomous vehicle control [7].”
Deep learning in neural networks: An overview (2014, methodology): “An important class of DS methods for NNs are Policy Gradient methods (Williams, 1986, 1988, 1992a; Baxter and Bartlett, 1999; Sutton et al., 1999a; Aberdeen, 2003; Ghavamzadeh and Mahadevan, 2003; Kohl and Stone, 2004; Wierstra et al., 2007, 2008; Rückstieß et al., 2008; Peters and Schaal, 2008b,a; ”
Reinforcement learning in robotics: A survey (2013, methodology): “In fact, many of the methods that scale to more interesting tasks are model-based (Atkeson et al, 1997; Abbeel et al, 2007) and often employ policy search rather than value function-based approaches (Gullapalli et al, 1994; Miyamoto et al, 1996; Kohl and Stone, 2004; Tedrake et al, 2005; Peters and ”
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots (2018, background): “Much research has applied reinforcement learning to locomotion tasks [2, 47, 11, 22, 31].”
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation (2018, methodology): “To name a few, Kohl et al. [24] introduce policy gradient RL method for locomotion of a four-legged robot.”
Deep spatial autoencoders for visuomotor learning (2015, methodology): “Reinforcement learning and policy search methods have been applied to perform a variety of robotic tasks, such as table tennis [9], object manipulation [10], [11], and locomotion [12], [13], [14], [15].”
Target-driven visual navigation in indoor scenes using deep reinforcement learning (2016, background): “[25] propose a policy gradient RL approach for locomotion of a four-legged robot.”
Cognitive Mapping and Planning for Visual Navigation (2017, methodology): “As an alternative to separating out discrete mapping and planning phases, reinforcement learning (RL) methods directly learn policies for robotic tasks (Kim et al. 2003; Peters and Schaal 2008; Kohl and Stone 2004).”
Path integral guided policy search (2016, methodology): “Reinforcement learning (RL) and policy search methods have shown considerable promise for enabling robots to automatically learn a wide range of complex skills [1, 2, 3, 4], and recent results in deep reinforcement learning suggest that this capability can be extended to learn nonlinear policies tha”
Learning contact-rich manipulation skills with guided policy search (2015, methodology): “Previous policy search methods in robotics generally use one or more of the following techniques to achieve good performance and reasonable training times: the use of example demonstrations [15], [9], [16], the use of a prior controller that allows the policy to only set high level targets, such as ”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1, except that C is now an FNN or RNN trained by evolutionary algorithms [200, 255, 105, 56, 68] applied to NNs [165, 321, 180, 259, 72, 90, 89, 110, 94], or by policy gradient methods [314, 315, 316, 274, 18, 1, 63, 128, 313, 210, 192, 191, 256, 85, 312, 190, 82, 93][245, Sec.”
A Brief Survey of Deep Reinforcement Learning (2017, neutral): “Although RL had some successes in the past [119, 109, 51, 79], previous approaches lacked scalablity and were inherently limited to fairly low-dimensional problems.”
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning (2016, methodology): “Policy gradient methods have been successfully applied to dialog systems (Jurˇcíˇcek et al., 2011), robotics (Kohl and Stone, 2004), and the board game Go (Silver et al., 2016).”
One Big Net For Everything (2018, background): “6.6], or reinforcement learning [29, 90, 101, 76] through policy gradients [104, 91, 3, 1, 13, 30, 103, 48, 82, 22, 102, 42] [76, Sec.”