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

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Showing 6901–6925 of 15113 papers

TitleStatusHype
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control TasksCode0
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space—0
Multibit Tries Packet Classification with Deep Reinforcement Learning—0
Moral reinforcement learning using actual causation—0
DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and GazeboCode0
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning—0
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning—0
Deep Apprenticeship Learning for Playing Games—0
A Deep Reinforcement Learning Blind AI in DareFightingICE—0
Attacking and Defending Deep Reinforcement Learning Policies—0
q-Munchausen Reinforcement Learning—0
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents—0
Rethinking Reinforcement Learning based Logic Synthesis—0
Towards on-sky adaptive optics control using reinforcement learning—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
Taming Continuous Posteriors for Latent Variational Dialogue Policies—0
Policy Gradient Method For Robust Reinforcement Learning—0
RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states—0
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning—0
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing—0
Unified Distributed EnvironmentCode0
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments—0
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
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Benchmark Results

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