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 3976–4000 of 15113 papers

TitleStatusHype
Design Principles of the Hippocampal Cognitive Map—0
Coordination-driven learning in multi-agent problem spaces—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Bayesian Bellman Operators—0
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning—0
Detecting and Adapting to Novelty in Games—0
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
Detecting Deceptive Reviews using Generative Adversarial Networks—0
Adaptive Stress Testing for Autonomous Vehicles—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Deterministic Value-Policy Gradients—0
DETERRENT: Detecting Trojans using Reinforcement Learning—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning—0
Developing cooperative policies for multi-stage reinforcement learning tasks—0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
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Benchmark Results

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