ICML 2024PastSafety & alignmentReinforcement learningTheory
ICML 2024 Workshop: Aligning Reinforcement Learning Experimentalists and Theorists
ARLET 2024
- Submission deadline
- Jun 1, 2024, 13:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (76)
Fetched from OpenReview (v2) on 2026-06-10.
A Case for Validation Buffer in Pessimistic Actor-Critic
A Theoretical Framework for Partially-Observed Reward States in RLHF
A Tractable Inference Perspective of Offline RL
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
Adaptive Foundation Models for Online Decisions: HyperAgent with Fast Incremental Uncertainty Estimation
Adaptive Two-Level Quasi-Monte Carlo for Soft Actor-Critic
Advantage Alignment Algorithms
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models
Batch Learning via Log-Sum-Exponential Estimator from Logged Bandit Feedback
Batched fixed-confidence pure exploration for bandits with switching constraints
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors
Coordination Failure in Cooperative Offline MARL
Decoupled Stochastic Gradient Descent for N-Player Games
Delayed Adversarial Attacks on Stochastic Multi-Armed Bandits
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
Dual Approximation Policy Optimization
Efficient Offline Learning of Ranking Policies via Top-$k$ Policy Decomposition
Efficient Offline Reinforcement Learning: The Critic is Critical
EMPO: A Clustering-Based On-Policy Algorithm for Offline Reinforcement Learing
Enhancing Actor-Critic Decision-Making with Afterstate Models for Continuous Control
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning
Functional Acceleration for Policy Mirror Descent
Generalized Linear Bandits with Limited Adaptivity
Handling Delay in Reinforcement Learning Caused by Parallel Computations of Neurons
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?
Improved Algorithms for Adversarial Bandits with Unbounded Losses
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
Information Theoretic Guarantees For Policy Alignment In Large Language Models
Is Value Learning Really the Main Bottleneck in Offline RL?
Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Learning to Steer Markovian Agents under Model Uncertainty
Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies
Markov Persuasion Processes: How to Persuade Multiple Agents From Scratch
Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO
Offline Reinforcement Learning with Pessimistic Value Priors
Offline RL via Feature-Occupancy Gradient Ascent
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics
Oracle-Efficient Reinforcement Learning for Max Value Ensembles
ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
Partially Observable Multi-Agent Reinforcement Learning using Mean Field Control
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
Policy Gradient Methods with Adaptive Policy Spaces
Provable Partially Observable Reinforcement Learning with Privileged Information
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
Quantized Representations Prevent Dimensional Collapse in Self-predictive RL
Realtime Reinforcement Learning: Towards Rapid Asynchronous Deployment of Large Models
REBEL: Reinforcement Learning via Regressing Relative Rewards
Reinforcement Learning from Bagged Reward
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity
Reward Centering
Reweighted Bellman Targets for Continual Reinforcement Learning
Risk-Aware Bandits for Best Crop Management
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
Safe exploration in reproducing kernel Hilbert spaces
Should You Trust DQN?
Survive on Planet Pandora: Robust Cross-Domain RL Under Distinct State-Action Representations
The Importance of Online Data: Understanding Preference Fine-Tuning via Coverage
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
Towards Zero-Shot Generalization in Offline Reinforcement Learning
Transductive Active Learning with Application to Safe Bayesian Optimization
Transferable Reinforcement Learning via Generalized Occupancy Models
VICtoR: Learning Hierarchical Vision-Instruction Correlation Rewards for Long-horizon Manipulation
vMF-exp: von Mises-Fisher Exploration of Large Action Sets with Hyperspherical Embeddings
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Wind farm control with cooperative multi-agent reinforcement learning