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

Showing 63016350 of 15113 papers

TitleStatusHype
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms0
LOCO: Adaptive exploration in reinforcement learning via local estimation of contraction coefficients0
Logarithmic regret bounds for continuous-time average-reward Markov decision processes0
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation0
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs0
Logical Composition in Lifelong Reinforcement Learning0
Logic and the 2-Simplicial Transformer0
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain0
Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers0
Logistic Q-Learning0
LogLLaMA: Transformer-based log anomaly detection with LLaMA0
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning0
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving0
Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks0
Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion0
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems0
Long-Range Indoor Navigation with PRM-RL0
Long Run Incremental Cost (LRIC) Distribution Network Pricing in UK, advising China's Distribution Network0
Long-Tail Classification for Distinctive Image Captioning: A Simple yet Effective Remedy for Side Effects of Reinforcement Learning0
Long Term Memory Network for Combinatorial Optimization Problems0
Long-term planning, short-term adjustments0
Long-term Safe Reinforcement Learning with Binary Feedback0
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach0
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function0
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention0
Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation0
Look Harder: A Neural Machine Translation Model with Hard Attention0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots0
LORD: Large Models based Opposite Reward Design for Autonomous Driving0
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning0
Loss Functions for Multiset Prediction0
Loss is its own Reward: Self-Supervision for Reinforcement Learning0
Loss of Plasticity in Continual Deep Reinforcement Learning0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems0
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics0
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces0
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization0
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning0
Low Emission Building Control with Zero-Shot Reinforcement Learning0
Low Entropy Communication in Multi-Agent Reinforcement Learning0
Lower Bounds for Learning in Revealing POMDPs0
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning0
Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning0
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery0
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing0
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities0
Deploying Offline Reinforcement Learning with Human Feedback0
Deploying Reinforcement Learning in Water Transport0
Depth and nonlinearity induce implicit exploration for RL0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing0
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Benchmark Results

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