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 9101–9150 of 15113 papers

TitleStatusHype
Interpretable Meta-Reinforcement Learning with Actor-Critic Method—0
Learning to Dynamically Select Between Reward Shaping Signals—0
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning—0
Explore with Dynamic Map: Graph Structured Reinforcement Learning—0
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets—0
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents—0
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
Divide-and-Conquer Monte Carlo Tree Search—0
Hindsight Curriculum Generation Based Multi-Goal Experience Replay—0
Combining Imitation and Reinforcement Learning with Free Energy Principle—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Hellinger Distance Constrained Regression—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Explicit Pareto Front Optimization for Constrained Reinforcement Learning—0
Deep Coherent Exploration For Continuous Control—0
Factored Action Spaces in Deep Reinforcement Learning—0
Addressing Extrapolation Error in Deep Offline Reinforcement Learning—0
Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.—0
Trust, but verify: model-based exploration in sparse reward environmentsCode0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
Structure and randomness in planning and reinforcement learningCode0
PAC-Bayesian Randomized Value Function with Informative Prior—0
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning—0
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems—0
Curriculum-based Deep Reinforcement Learning for Quantum Control—0
Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Relational Deep Reinforcement Learning for Routing in Wireless Networks—0
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration—0
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Model-Based Visual Planning with Self-Supervised Functional DistancesCode1
Is Pessimism Provably Efficient for Offline RL?—0
Reinforcement Learning for Control of ValvesCode1
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning—0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing—0
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy—0
Portfolio Optimization with 2D Relative-Attentional Gated Transformer—0
Stability-Certified Reinforcement Learning via Spectral Normalization—0
POPO: Pessimistic Offline Policy OptimizationCode0
Towards sample-efficient episodic control with DAC-ML—0
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Deep Reinforcement Learning for Long-Short Portfolio OptimizationCode0
Towards Continual Reinforcement Learning: A Review and Perspectives—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
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Benchmark Results

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