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 12451–12500 of 15113 papers

TitleStatusHype
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives—0
Ranking Policy GradientCode0
Proximal Distilled Evolutionary Reinforcement LearningCode0
Modern Deep Reinforcement Learning AlgorithmsCode0
Optimal Use of Experience in First Person Shooter Environments—0
Event-Driven Models—0
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals—0
Deep Conservative Policy Iteration—0
Inverse reinforcement learning conditioned on brain scan—0
A Theoretical Connection Between Statistical Physics and Reinforcement Learning—0
Neural networks with motivation—0
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations—0
A neurally plausible model learns successor representations in partially observable environmentsCode0
Disentangled Skill Embeddings for Reinforcement Learning—0
Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation—0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
A Study of State Aliasing in Structured Prediction with RNNs—0
Shaping Belief States with Generative Environment Models for RL—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks—0
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework—0
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning—0
A Deep Reinforcement Learning Approach for Global RoutingCode0
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach—0
A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement LearningCode0
Cooperative Lane Changing via Deep Reinforcement Learning—0
Calibrated Model-Based Deep Reinforcement LearningCode0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
Experience Replay Optimization—0
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing—0
Wasserstein Adversarial Imitation Learning—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
Robust Reinforcement Learning for Continuous Control with Model Misspecification—0
Sample-efficient Adversarial Imitation Learning from Observation—0
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration—0
Towards White-box Benchmarks for Algorithm Control—0
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination—0
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning—0
Hill Climbing on Value Estimates for Search-control in Dyna—0
Directed Exploration for Reinforcement Learning—0
Language as an Abstraction for Hierarchical Deep Reinforcement LearningCode0
A gray-box approach for curriculum learning—0
Learning-Driven Exploration for Reinforcement LearningCode0
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer—0
A Joint Planning and Learning Framework for Human-Aided Decision-Making—0
Universal Successor Features Based Deep Reinforcement Learning for Navigation—0
LPaintB: Learning to Paint from Self-Supervision—0
Reinforcement Learning Driven Heuristic Optimization—0
Reinforcement Learning with Non-uniform State Representations for Adaptive Search—0
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Benchmark Results

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