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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5351–5375 of 15113 papers

TitleStatusHype
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning—0
RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources—0
Risk-Averse Bayes-Adaptive Reinforcement Learning—0
Risk-Averse Learning by Temporal Difference Methods—0
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Risk Averse Robust Adversarial Reinforcement Learning—0
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning—0
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search—0
Risk-Aware Reinforcement Learning through Optimal Transport Theory—0
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems—0
Risk-Aware Transfer in Reinforcement Learning using Successor Features—0
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning—0
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning—0
Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation—0
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria—0
Risk Perspective Exploration in Distributional Reinforcement Learning—0
Risk-Sensitive and Robust Model-Based Reinforcement Learning and Planning—0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning—0
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy—0
Risk-sensitive Markov Decision Process and Learning under General Utility Functions—0
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning—0
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Benchmark Results

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