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

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Showing 6501–6525 of 15113 papers

TitleStatusHype
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control OptimizationCode1
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster LearningCode0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Sequential memory improves sample and memory efficiency in Episodic ControlCode0
Control Theoretic Analysis of Temporal Difference Learning—0
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
Embodied Learning for Lifelong Visual Perception—0
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations—0
Exponential Family Model-Based Reinforcement Learning via Score MatchingCode0
Robustness and risk management via distributional dynamic programming—0
The Statistical Complexity of Interactive Decision Making—0
Safe Reinforcement Learning with Chance-constrained Model Predictive Control—0
RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes—0
Multiagent Model-based Credit Assignment for Continuous Control—0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
Intelligent Traffic Light via Policy-based Deep Reinforcement LearningCode0
Improving the Performance of Backward Chained Behavior Trees that use Reinforcement LearningCode0
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?—0
Abstractions of General Reinforcement Learning—0
Reinforcement Learning with Dynamic Convex Risk MeasuresCode1
Neuro-Symbolic Hierarchical Rule Induction—0
Reducing Planning Complexity of General Reinforcement Learning with Non-Markovian Abstractions—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
A Survey on Interpretable Reinforcement Learning—0
Lane Change Decision-Making through Deep Reinforcement LearningCode1
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Benchmark Results

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