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 9676–9700 of 15113 papers

TitleStatusHype
Prior Preference Learning From Experts: Designing A Reward with Active Inference—0
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning—0
Optimizing Information Bottleneck in Reinforcement Learning: A Stein Variational Approach—0
PODS: Policy Optimization via Differentiable Simulation—0
Plan-Based Asymptotically Equivalent Reward Shaping—0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic—0
Trust, but verify: model-based exploration in sparse reward environmentsCode0
Monte-Carlo Planning and Learning with Language Action Value Estimates—0
Meta-Reinforcement Learning With Informed Policy Regularization—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Reinforcement Learning Based Asymmetrical DNN Modularization for Optimal Loading—0
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous ControlCode0
Practical Marginalized Importance Sampling with the Successor Representation—0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty—0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward—0
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study—0
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning—0
Uncertainty Weighted Offline Reinforcement Learning—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Optimistic Policy Optimization with General Function Approximations—0
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Offline Policy Optimization with Variance Regularization—0
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Benchmark Results

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