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 11851–11900 of 15113 papers

TitleStatusHype
Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes—0
Pre-training as Batch Meta Reinforcement Learning with tiMe—0
Sequence-level Intrinsic Exploration Model for Partially Observable Domains—0
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee—0
Counterfactual Regularization for Model-Based Reinforcement Learning—0
Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities—0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search—0
Efficient meta reinforcement learning via meta goal generation—0
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning—0
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning—0
HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNING—0
Collaborative Inter-agent Knowledge Distillation for Reinforcement Learning—0
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning—0
Assessing Generalization in TD methods for Deep Reinforcement Learning—0
Behavior-Guided Reinforcement Learning—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
Learning by shaking: Computing policy gradients by physical forward-propagation—0
Contextual Inverse Reinforcement Learning—0
Adapt-to-Learn: Policy Transfer in Reinforcement Learning—0
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer—0
Attention Privileged Reinforcement Learning for Domain Transfer—0
CAPACITY-LIMITED REINFORCEMENT LEARNING: APPLICATIONS IN DEEP ACTOR-CRITIC METHODS FOR CONTINUOUS CONTROL—0
AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING—0
Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling—0
How many weights are enough : can tensor factorization learn efficient policies ?—0
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search—0
Do recent advancements in model-based deep reinforcement learning really improve data efficiency?—0
CrossNorm: On Normalization for Off-Policy Reinforcement Learning—0
Learning Good Policies By Learning Good Perceptual Models—0
Generalizing Reinforcement Learning to Unseen Actions—0
Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
Learning Temporal Abstraction with Information-theoretic Constraints for Hierarchical Reinforcement Learning—0
City Metro Network Expansion with Reinforcement Learning—0
Learning to Reach Goals Without Reinforcement Learning—0
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents—0
Learning World Graph Decompositions To Accelerate Reinforcement Learning—0
Learning with Social Influence through Interior Policy Differentiation—0
Event Discovery for History Representation in Reinforcement Learning—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning—0
Robust Domain Randomization for Reinforcement Learning—0
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning—0
Trajectory representation learning for Multi-Task NMRDPs planning—0
Policy Tree Network—0
Training a Constrained Natural Media Painting Agent using Reinforcement Learning—0
S2VG: Soft Stochastic Value Gradient method—0
Subjective Reinforcement Learning for Open Complex Environments—0
Mint: Matrix-Interleaving for Multi-Task Learning—0
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Benchmark Results

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