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 9076–9100 of 15113 papers

TitleStatusHype
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
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning—0
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization—0
Pretraining & Reinforcement Learning: Sharpening the Axe Before Cutting the Tree—0
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning—0
Preventing Imitation Learning with Adversarial Policy Ensembles—0
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach—0
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Benchmark Results

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