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 5826–5850 of 15113 papers

TitleStatusHype
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(Δ)—0
Segregation Dynamics with Reinforcement Learning and Agent Based Modeling—0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning—0
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning—0
Selecting the State-Representation in Reinforcement Learning—0
Selective Credit Assignment—0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
Selective Particle Attention: Visual Feature-Based Attention in Deep Reinforcement Learning—0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Selective Token Generation for Few-shot Language Modeling—0
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Selective Uncertainty Propagation in Offline RL—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks—0
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Self-Consistent Models and Values—0
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings—0
Self-Critical Alternate Learning based Semantic Broadcast Communication—0
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Benchmark Results

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