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 8201–8225 of 15113 papers

TitleStatusHype
Convergent and Efficient Deep Q Learning Algorithm—0
Learning Invariant Reward Functions through Trajectory Interventions—0
Auto-Encoding Inverse Reinforcement Learning—0
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping—0
An Experimental Design Perspective on Exploration in Reinforcement Learning—0
Assessing Deep Reinforcement Learning Policies via Natural Corruptions at the Edge of Imperceptibility—0
Generalizing Successor Features to continuous domains for Multi-task Learning—0
Bi-linear Value Networks for Multi-goal Reinforcement Learning—0
Distributional Decision Transformer for Hindsight Information Matching—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Efficient Wasserstein and Sinkhorn Policy Optimization—0
Disentangling Generalization in Reinforcement Learning—0
Fourier Features in Reinforcement Learning with Neural NetworksCode0
Formulation and validation of a car-following model based on deep reinforcement learning—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
CubeTR: Learning to Solve the Rubik's Cube using Transformers—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning—0
Efficient Reinforcement Learning Experimentation in PyTorch—0
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning—0
Learning Controllable Elements Oriented Representations for Reinforcement Learning—0
Distributional Reinforcement Learning with Monotonic Splines—0
Better state exploration using action sequence equivalence—0
An Optics Controlling Environment and Reinforcement Learning Benchmarks—0
Evaluating Robustness of Cooperative MARL—0
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Benchmark Results

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