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 12751–12800 of 15113 papers

TitleStatusHype
Neural Program Planner for Structured Predictions—0
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions—0
Symbolic Regression Methods for Reinforcement Learning—0
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study—0
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction—0
DQN with model-based exploration: efficient learning on environments with sparse rewards—0
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement LearningCode1
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention—0
Jet grooming through reinforcement learningCode0
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Single-step Options for Adversary Driving—0
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learningCode0
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation—0
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context VariablesCode0
Hindsight Generative Adversarial Imitation Learning—0
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL—0
Deep Reinforcement Learning with Decorrelation—0
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts—0
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents—0
Learning proposals for sequential importance samplers using reinforced variational inference—0
Robust Reinforcement Learning for Autonomous Driving—0
Multi-agent query reformulation: Challenges and the role of diversity—0
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning—0
Policy Distillation and Value Matching in Multiagent Reinforcement Learning—0
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement LearningCode0
Can User-Centered Reinforcement Learning Allow a Robot to Attract Passersby without Causing Discomfort?—0
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and GazeboCode0
No-regret Exploration in Contextual Reinforcement Learning—0
Deep Reinforcement Learning with Feedback-based ExplorationCode0
On Applications of Bootstrap in Continuous Space Reinforcement Learning—0
Reinforcement Learning with Dynamic Boltzmann Softmax UpdatesCode0
ROS2Learn: a reinforcement learning framework for ROS 2Code0
CoaCor: Code Annotation for Code Retrieval with Reinforcement LearningCode0
Effective reinforcement learning based local search for the maximum k-plex problem—0
Task-oriented Design through Deep Reinforcement Learning—0
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems—0
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning—0
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented LearningCode0
A Review of Reinforcement Learning for Autonomous Building Energy Management—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Hybrid Reinforcement Learning with Expert State SequencesCode0
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling—0
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy CriticsCode0
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal ControlCode0
Learning to Paint With Model-based Deep Reinforcement LearningCode1
Deep learning for molecular design - a review of the state of the art—0
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning—0
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Benchmark Results

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