A new marginalized reparameterization estimator allows low-variance training of mixture policies in entropy-regularized actor-critic algorithms, matching or exceeding Gaussian policy performance in several continuous control benchmarks.
Multi-goal reinforcement learn- ing: Challenging robotics environments and request for research.arXiv preprint arXiv:1802.09464
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abstract
The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include pushing, sliding and pick & place with a Fetch robotic arm as well as in-hand object manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent is told what to do using an additional input. The second part of the paper presents a set of concrete research ideas for improving RL algorithms, most of which are related to Multi-Goal RL and Hindsight Experience Replay.
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UNVERDICTED 5representative citing papers
Introduces RAPCs and a contraction Bellman operator that jointly enforce probabilistic reach-avoid constraints while minimizing expected costs in stochastic RL, with almost-sure convergence to local optima.
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
Trajectory-based data augmentation exploits geometric relationships between rewards, values, and logging policies to enable effective offline RL from few suboptimal trajectories.
Middle-mile logistics is cast as a multi-object goal-conditioned MDP and solved by combining graph neural networks with model-free RL via extraction of small feature graphs.
citing papers explorer
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Revisiting Mixture Policies in Entropy-Regularized Actor-Critic
A new marginalized reparameterization estimator allows low-variance training of mixture policies in entropy-regularized actor-critic algorithms, matching or exceeding Gaussian policy performance in several continuous control benchmarks.
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Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
Introduces RAPCs and a contraction Bellman operator that jointly enforce probabilistic reach-avoid constraints while minimizing expected costs in stochastic RL, with almost-sure convergence to local optima.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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Trajectory-Level Data Augmentation for Offline Reinforcement Learning
Trajectory-based data augmentation exploits geometric relationships between rewards, values, and logging policies to enable effective offline RL from few suboptimal trajectories.
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Middle-mile logistics through the lens of goal-conditioned reinforcement learning
Middle-mile logistics is cast as a multi-object goal-conditioned MDP and solved by combining graph neural networks with model-free RL via extraction of small feature graphs.