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06/23/2023
Maintaining Plasticity in Deep Continual Learning
Modern deep-learning systems are specialized to problem settings in whic...
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06/23/2023
Correcting discount-factor mismatch in on-policy policy gradient methods
The policy gradient theorem gives a convenient form of the policy gradie...
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05/29/2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
We present a scalable and effective exploration strategy based on Thomps...
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05/09/2023
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization
Continuous-time reinforcement learning tasks commonly use discrete steps...
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02/07/2023
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning
Modern representation learning methods may fail to adapt quickly under n...
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02/03/2023
Learning to Optimize for Reinforcement Learning
In recent years, by leveraging more data, computation, and diverse tasks...
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12/06/2022
Variable-Decision Frequency Option Critic
In classic reinforcement learning algorithms, agents make decisions at d...
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10/20/2022
HesScale: Scalable Computation of Hessian Diagonals
Second-order optimization uses curvature information about the objective...
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10/05/2022
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers
Real-time learning is crucial for robotic agents adapting to ever-changi...
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05/22/2022
Memory-efficient Reinforcement Learning with Knowledge Consolidation
Artificial neural networks are promising as general function approximato...
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03/23/2022
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots
An oft-ignored challenge of real-world reinforcement learning is that th...
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02/04/2022
A Temporal-Difference Approach to Policy Gradient Estimation
The policy gradient theorem (Sutton et al., 2000) prescribes the usage o...
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12/22/2021
An Alternate Policy Gradient Estimator for Softmax Policies
Policy gradient (PG) estimators for softmax policies are ineffective wit...
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08/13/2021
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness
The Backprop algorithm for learning in neural networks utilizes two mech...
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07/17/2021
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Approximate Policy Iteration (API) algorithms alternate between (approxi...
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06/10/2021
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control
Designing adaptable control laws that can transfer between different rob...
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03/09/2021
Model-free Policy Learning with Reward Gradients
Policy gradient methods estimate the gradient of a policy objective sole...
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03/27/2019
Autoregressive Policies for Continuous Control Deep Reinforcement Learning
Reinforcement learning algorithms rely on exploration to discover new be...
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09/20/2018
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
Through many recent successes in simulation, model-free reinforcement le...
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03/19/2018
Setting up a Reinforcement Learning Task with a Real-World Robot
Reinforcement learning is a promising approach to developing hard-to-eng...
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12/13/2015
True Online Temporal-Difference Learning
The temporal-difference methods TD(λ) and Sarsa(λ) form a core part of m...
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07/01/2015
