Reinforcement Learning
Simple random search of static linear policies is competitive for reinforcement learning
Horia Mania, Aurelia Guy, Benjamin Recht
Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
Neural Information Processing Systems http://nips.cc/
Learning Task Specifications from Demonstrations
Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit Seshia
Neural Information Processing Systems http://nips.cc/
Transfer of Deep Reactive Policies for MDP Planning
Aniket (Nick) Bajpai, Sankalp Garg, None
Neural Information Processing Systems http://nips.cc/
Text-AwareDiffusionforPolicyLearning
Training an agent to achieve particular goals or perform desired behaviors is often accomplished through reinforcement learning, especially in the absence of expert demonstrations. However, supporting novel goals or behaviors through reinforcement learning requires the ad-hoc design of appropriate reward functions, which quickly becomes intractable. Toaddress thischallenge, wepropose Text-AwareDiffusion forPolicyLearning (TADPoLe), which uses apretrained, frozen text-conditioned diffusion model to compute dense zero-shot reward signals for text-aligned policy learning.
Reconciling ฮป-Returns with Experience Replay
Brett Daley, Christopher Amato
A unique benefit to this approach is that each transition's TD error can be
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
Deep Reinforcement Learning of Marked Temporal Point Processes
Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez
Neural Information Processing Systems http://nips.cc/
You Can't Counton Luck: Why Decision Transformersand RvS Failin Stochastic Environments
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