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 7601–7625 of 15113 papers

TitleStatusHype
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
Control Theoretic Analysis of Temporal Difference Learning—0
Sequential memory improves sample and memory efficiency in Episodic ControlCode0
Robustness and risk management via distributional dynamic programming—0
Exponential Family Model-Based Reinforcement Learning via Score MatchingCode0
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations—0
Embodied Learning for Lifelong Visual Perception—0
Improving the Performance of Backward Chained Behavior Trees that use Reinforcement LearningCode0
Intelligent Traffic Light via Policy-based Deep Reinforcement LearningCode0
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?—0
Multiagent Model-based Credit Assignment for Continuous Control—0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
Safe Reinforcement Learning with Chance-constrained Model Predictive Control—0
The Statistical Complexity of Interactive Decision Making—0
RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes—0
Reducing Planning Complexity of General Reinforcement Learning with Non-Markovian Abstractions—0
Neuro-Symbolic Hierarchical Rule Induction—0
Abstractions of General Reinforcement Learning—0
A Survey on Interpretable Reinforcement Learning—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
On the Unreasonable Efficiency of State Space Clustering in Personalization TasksCode0
Rediscovering Affordance: A Reinforcement Learning Perspective—0
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning—0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
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Benchmark Results

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