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 6601–6625 of 15113 papers

TitleStatusHype
Functional Optimization Reinforcement Learning for Real-Time Bidding—0
Guided Exploration in Reinforcement Learning via Monte Carlo Critic OptimizationCode0
Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation—0
Dynamic network congestion pricing based on deep reinforcement learning—0
Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach—0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach—0
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning—0
Reinforcement learning based adaptive metaheuristicsCode0
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach—0
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems—0
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents—0
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation—0
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality—0
Reinforcement Learning under Partial Observability Guided by Learned Environment Models—0
Recursive Reinforcement Learning—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations—0
CGAR: Critic Guided Action Redistribution in Reinforcement LeaningCode0
Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision—0
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement LearningCode0
Auto-Encoding Adversarial Imitation Learning—0
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Benchmark Results

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