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 12001–12050 of 15113 papers

TitleStatusHype
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling—0
Harnessing Structures for Value-Based Planning and Reinforcement LearningCode0
Scaling data-driven robotics with reward sketching and batch reinforcement learning—0
Visual Exploration and Energy-aware Path Planning via Reinforcement LearningCode0
CAQL: Continuous Action Q-Learning—0
MERL: Multi-Head Reinforcement Learning—0
Learning to Reach Goals Without Reinforcement Learning—0
City Metro Network Expansion with Reinforcement Learning—0
Generalizing Reinforcement Learning to Unseen Actions—0
Assessing Generalization in TD methods for Deep Reinforcement Learning—0
Adapt-to-Learn: Policy Transfer in Reinforcement Learning—0
AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING—0
Learning by shaking: Computing policy gradients by physical forward-propagation—0
Collaborative Inter-agent Knowledge Distillation for Reinforcement Learning—0
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning—0
Counterfactual Regularization for Model-Based Reinforcement Learning—0
Learning World Graph Decompositions To Accelerate Reinforcement Learning—0
C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement LearningCode0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
CAPACITY-LIMITED REINFORCEMENT LEARNING: APPLICATIONS IN DEEP ACTOR-CRITIC METHODS FOR CONTINUOUS CONTROL—0
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search—0
Learning with Social Influence through Interior Policy Differentiation—0
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning—0
Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling—0
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer—0
Data Valuation using Reinforcement LearningCode0
How many weights are enough : can tensor factorization learn efficient policies ?—0
Efficient meta reinforcement learning via meta goal generation—0
Learning Temporal Abstraction with Information-theoretic Constraints for Hierarchical Reinforcement Learning—0
Do recent advancements in model-based deep reinforcement learning really improve data efficiency?—0
Learning Good Policies By Learning Good Perceptual Models—0
Long-term planning, short-term adjustments—0
Attention Privileged Reinforcement Learning for Domain Transfer—0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search—0
Behavior-Guided Reinforcement Learning—0
Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities—0
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning—0
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents—0
Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks—0
Event Discovery for History Representation in Reinforcement Learning—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Contextual Inverse Reinforcement Learning—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNING—0
CrossNorm: On Normalization for Off-Policy Reinforcement Learning—0
Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning—0
Trajectory representation learning for Multi-Task NMRDPs planning—0
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning—0
Multi-task Batch Reinforcement Learning with Metric Learning—0
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Benchmark Results

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