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 1351–1360 of 15113 papers

TitleStatusHype
Deterministic Exploration via Stationary Bellman Error Maximization—0
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement LearningCode0
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs—0
Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone ConnectivityCode1
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision TransformersCode1
Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode—0
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation—0
Online Intrinsic Rewards for Decision Making Agents from Large Language Model FeedbackCode1
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning—0
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Benchmark Results

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