SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
Reinforcement learning in robotics: A survey,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Develops a distributed primal-dual actor-critic method for constrained multi-agent RL with general parameterization, proves consensus and convergence to an equilibrium, analyzes sub-optimality, and introduces a constrained Cournot game testbed.
Deep reinforcement learning learns robust policies for flexible robots but is sensitive to sensor choice.
citing papers explorer
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SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
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A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization
Develops a distributed primal-dual actor-critic method for constrained multi-agent RL with general parameterization, proves consensus and convergence to an equilibrium, analyzes sub-optimality, and introduces a constrained Cournot game testbed.
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On Training Flexible Robots using Deep Reinforcement Learning
Deep reinforcement learning learns robust policies for flexible robots but is sensitive to sensor choice.