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 8451–8475 of 15113 papers

TitleStatusHype
Predictive Representation Learning for Language Modeling—0
Predictive State Temporal Difference Learning—0
Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning—0
Predictor-Corrector(PC) Temporal Difference(TD) Learning (PCTD)—0
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning—0
Learning-to-defer for sequential medical decision-making under uncertainty—0
Preference-based Reinforcement Learning with Finite-Time Guarantees—0
Preference Elicitation for Offline Reinforcement Learning—0
Preference Optimization for Combinatorial Optimization Problems—0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning—0
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning—0
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation—0
Preserving Expert-Level Privacy in Offline Reinforcement Learning—0
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning—0
Pre-trained Visual Dynamics Representations for Efficient Policy Learning—0
Pretrained Visual Representations in Reinforcement Learning—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Multi-task Batch Reinforcement Learning with Metric Learning—0
Pre-training as Batch Meta Reinforcement Learning with tiMe—0
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning—0
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations—0
Pretraining for Language Conditioned Imitation with Transformers—0
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition—0
Pretraining in Deep Reinforcement Learning: A Survey—0
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Benchmark Results

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