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 6001–6025 of 15113 papers

TitleStatusHype
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning—0
Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics—0
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
Single Cell Training on Architecture Search for Image Denoising—0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence—0
Single-Shot Pruning for Offline Reinforcement Learning—0
Data-Incremental Continual Offline Reinforcement Learning—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
STEEL: Singularity-aware Reinforcement Learning—0
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning—0
Sketch-Based Linear Value Function Approximation—0
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches—0
Skill-based Meta-Reinforcement Learning—0
Skill-based Model-based Reinforcement Learning—0
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning—0
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning—0
Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations—0
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration—0
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning—0
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Benchmark Results

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