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 4651–4675 of 15113 papers

TitleStatusHype
Regret Bounds for Risk-Sensitive Reinforcement Learning—0
Multi-Object Navigation with dynamically learned neural implicit representationsCode1
Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation ControlCode1
Simulating Coverage Path Planning with Roomba—0
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies—0
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems—0
A policy gradient approach for Finite Horizon Constrained Markov Decision ProcessesCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Benchmarking Reinforcement Learning Techniques for Autonomous NavigationCode1
A Comprehensive Survey of Data Augmentation in Visual Reinforcement LearningCode1
In-Hand Object Rotation via Rapid Motor AdaptationCode2
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning—0
Experiential Explanations for Reinforcement LearningCode0
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control—0
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
The Role of Coverage in Online Reinforcement Learning—0
Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweeningCode1
State Advantage Weighting for Offline RL—0
Dynamically meeting performance objectives for multiple services on a service mesh—0
Cognitive Models as Simulators: The Case of Moral Decision-Making—0
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement LearningCode1
Large Language Models can Implement Policy Iteration—0
Conservative Bayesian Model-Based Value Expansion for Offline Policy OptimizationCode0
Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning ApproachCode0
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Benchmark Results

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