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 3226–3250 of 15113 papers

TitleStatusHype
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation—0
Neural Inventory Control in Networks via Hindsight Differentiable Policy OptimizationCode1
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap—0
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Adaptive Ordered Information Extraction with Deep Reinforcement LearningCode0
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement LearningCode1
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agentsCode0
On the Model-Misspecification in Reinforcement Learning—0
Enhancing variational quantum state diagonalization using reinforcement learning techniquesCode0
Acceleration in Policy Optimization—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
Active Policy Improvement from Multiple Black-box OraclesCode0
Genes in Intelligent AgentsCode0
Do as I can, not as I get—0
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement—0
Bootstrapped Representations in Reinforcement Learning—0
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAXCode2
Semi-Offline Reinforcement Learning for Optimized Text GenerationCode0
Temporal Difference Learning with Experience Replay—0
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Granger Causal Interaction Skill Chains—0
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Benchmark Results

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