SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 11051–11075 of 15113 papers

TitleStatusHype
Generalization in Deep RL for TSP Problems via Equivariance and Local Search—0
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning—0
Generalization in Generation: A closer look at Exposure Bias—0
Generalization in Monitored Markov Decision Processes (Mon-MDPs)—0
Generalization in Transfer Learning—0
Generalization of Compositional Tasks with Logical Specification via Implicit Planning—0
Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation—0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Generalization Through the Lens of Learning Dynamics—0
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly—0
Generalized Hindsight for Reinforcement Learning—0
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning—0
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping—0
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
Generalized Off-Policy Actor-Critic—0
Generalized Planning With Deep Reinforcement Learning—0
Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts—0
Generalized Reinforcement Learning for Building Control using Behavioral Cloning—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization—0
Generalizing Curricula for Reinforcement Learning—0
Generalizing from a few environments in safety-critical reinforcement learning—0
Generalizing Reinforcement Learning to Unseen Actions—0
Generalizing Skills with Semi-Supervised Reinforcement Learning—0
Generalizing Successor Features to continuous domains for Multi-task Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified