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 8251–8275 of 15113 papers

TitleStatusHype
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning—0
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification—0
Interpreting Reinforcement Policies through Local Behaviors—0
Adaptive Graph Capsule Convolutional Networks—0
Bayesian Exploration for Lifelong Reinforcement Learning—0
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment—0
Combinatorial Reinforcement Learning Based Scheduling for DNN Execution on Edge—0
Joint Self-Supervised Learning for Vision-based Reinforcement Learning—0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning—0
Faster Reinforcement Learning with Value Target Lower Bounding—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
DiBB: Distributing Black-Box Optimization—0
Closed-Loop Control of Additive Manufacturing via Reinforcement Learning—0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
Boosted Curriculum Reinforcement Learning—0
Reachability Traces for Curriculum Design in Reinforcement Learning—0
Text Generation with Efficient (Soft) Q-Learning—0
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities—0
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning—0
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Sequential Communication in Multi-Agent Reinforcement Learning—0
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers—0
The Essential Elements of Offline RL via Supervised Learning—0
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning—0
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Benchmark Results

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