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 7201–7225 of 15113 papers

TitleStatusHype
Stability Constrained Reinforcement Learning for Real-Time Voltage Control—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Reinforcement Learning with Information-Theoretic Actuation—0
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement LearningCode1
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
Scalable Online Planning via Reinforcement Learning Fine-TuningCode1
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators—0
Surveillance Evasion Through Bayesian Reinforcement LearningCode0
A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping—0
CubeTR: Learning to Solve the Rubik's Cube using Transformers—0
Policy improvement by planning with GumbelCode2
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
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Variational oracle guiding for reinforcement learning—0
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization—0
WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Particle Based Stochastic Policy Optimization—0
Value Refinement Network (VRN)—0
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization—0
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Benchmark Results

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