关于ICLR2022
In 2022, in an effort to broaden the diversity of the pool of participants to ICLR 2022, we are starting a program specifically assisting underrepresented, underprivileged, independent, and particularly first-time ICLR submitters. We hope this program can help create a path for prospective ICLR authors—who would not otherwise have considered participating in, or working on a submission to ICLR—to join the ICLR community, find project ideas, collaborators, mentorship and computational support throughout the submission process, and establish valuable connections and first-hand training during their early career.
Our goal is to provide underrepresented minorities, especially first-timers, and independent researchers, a taste of first-hand research experiences within a community, and a clear target to work towards. For that we need experienced researchers to join this program to provide starter ideas, ongoing feedback, lightweight mentorship, all the way to fully engaged collaboration.
重要时间节点
提交的强化学习论文列表
1. Learning Sampling Policy for Faster Derivative Free Optimization
2. IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data
3. Pessimistic Model Selection for Offline Deep Reinforcement Learning
4. Robust Imitation via Mirror Descent Inverse Reinforcement Learning
5. Optimizing Few-Step Diffusion Samplers by Gradient Descent
6. Offline Pre-trained Multi-Agent Decision Transformer
7. Tesseract: Gradient Flip Score to Secure Federated Learning against Model Poisoning Attacks
8. DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
9. Skill-based Meta-Reinforcement Learning
10. Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
11. Faster Reinforcement Learning with Value Target Lower Bounding
12. On Distributed Adaptive Optimization with Gradient Compression
13. Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
14. DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
15. An Experimental Design Perspective on Exploration in Reinforcement Learning
16. Zero-Shot Reward Specification via Grounded Natural Language
17. Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach
18. Generalisation in Lifelong Reinforcement Learning through Logical Composition
19. Distributional Reinforcement Learning with Monotonic Splines
20. Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
21. Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
22. Programmatic Reinforcement Learning without Oracles
23. Containerized Distributed Value-Based Multi-Agent Reinforcement Learning
24. PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
25. Bi-linear Value Networks for Multi-goal Reinforcement Learning
26. Pareto Policy Pool for Model-based Offline Reinforcement Learning
27. Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning
28. A Simple Reward-free Approach to Constrained Reinforcement Learning
29. Decentralized Cross-Entropy Method for Model-Based Reinforcement Learning
30. The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning
31. COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
32. Stochastic Reweighted Gradient Descent
33. Stochastic Reweighted Gradient Descent
34. EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
35. A First-Order Method for Estimating Natural Gradients for Variational Inference with Gaussians and Gaussian Mixture Models
36. Reinforcement Learning with Predictive Consistent Representations
37. Offline-Online Reinforcement Learning: Extending Batch and Online RL
38. DisTop: Discovering a Topological representation to learn diverse and rewarding skills
39. OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning
40. LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
41. Projective Manifold Gradient Layer for Deep Rotation Regression
42. Learning Invariant Reward Functions through Trajectory Interventions
43. A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
44. Reachability Traces for Curriculum Design in Reinforcement Learning
45. When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
46. The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models
47. EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
48. A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
49. The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
50. Finding General Equilibria in Many-Agent Economic Simulations using Deep Reinforcement Learning
51. Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
52. Accelerated Policy Learning with Parallel Differentiable Simulation
53. Learning Transferable Reward for Query Object Localization with Policy Adaptation
54. A Risk-Sensitive Policy Gradient Method
55. Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks
56. Know Your Action Set: Learning Action Relations for Reinforcement Learning
57. Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos
58. The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program
59. Variational Wasserstein gradient flow
60. GradMax: Growing Neural Networks using Gradient Information
61. SPLID: Self-Imitation Policy Learning through Iterative Distillation
62. Goal Randomization for Playing Text-based Games without a Reward Function
63. Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
64. Second-Order Rewards For Successor Features
65. StARformer: Transformer with State-Action-Reward Representations
66. Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning
67. MixRL: Data Mixing Augmentation for Regression using Reinforcement Learning
68. Safety-aware Policy Optimisation for Autonomous Racing
69. Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws
70. Low-Precision Stochastic Gradient Langevin Dynamics
71. Autonomous Reinforcement Learning: Formalism and Benchmarking
72. Gradient Assisted Learning
73. A Boosting Approach to Reinforcement Learning
74. Should I Run Offline Reinforcement Learning or Behavioral Cloning?
75. Disentangling Generalization in Reinforcement Learning
76. Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
77. Variance Reduced Domain Randomization for Policy Gradient
78. Learning Symmetric Locomotion using Cumulative Fatigue for Reinforcement Learning
79. Reinforcement Learning with Efficient Active Feature Acquisition
80. Xi-learning: Successor Feature Transfer Learning for General Reward Functions
81. On the Implicit Biases of Architecture & Gradient Descent
82. CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
83. Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
84. NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training
85. Transformers are Meta-Reinforcement Learners
86. Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning
87. Memory-Constrained Policy Optimization
88. Language Model Pre-training Improves Generalization in Policy Learning
89. Explaining Off-Policy Actor-Critic From A Bias-Variance Perspective
90. SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
91. Deep Inverse Reinforcement Learning via Adversarial One-Class Classification
92. Joint Self-Supervised Learning for Vision-based Reinforcement Learning
93. Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
94. Learning When and What to Ask: a Hierarchical Reinforcement Learning Framework
95. SS-MAIL: Self-Supervised Multi-Agent Imitation Learning
96. Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games
97. Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward
98. Beyond Prioritized Replay: Sampling States in Model-Based Reinforcement Learning via Simulated Priorities
99. DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
100. Learning State Representations via Retracing in Reinforcement Learning
101. State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning
102. State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning
103. Asynchronous Multi-Agent Actor-Critic with Macro-Actions
104. Adapting Stepsizes by Momentumized Gradients Improves Optimization and Generalization
105. SparRL: Graph Sparsification via Deep Reinforcement Learning
106. Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning
107. Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
108. OSSuM: A Gradient-Free Approach For Pruning Neural Networks At Initialization
109. Adversarial Unlearning of Backdoors via Implicit Hypergradient
110. Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization
111. Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning
112. Offline Reinforcement Learning for Large Scale Language Action Spaces
113. Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs
114. Softmax Gradient Tampering: Decoupling the Backward Pass for Improved Fitting
115. Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
116. Closed-Loop Control of Additive Manufacturing via Reinforcement Learning
117. Generalizing Few-Shot NAS with Gradient Matching
118. Recurrent Model-Free RL is a Strong Baseline for Many POMDPs
119. THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
120. A First-Occupancy Representation for Reinforcement Learning
121. Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
122. SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
123. Modular Lifelong Reinforcement Learning via Neural Composition
124. Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
125. Policy Smoothing for Provably Robust Reinforcement Learning
126. Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding
127. MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning
128. CoMPS: Continual Meta Policy Search
129. SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition
130. Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning
131. Benchmarking Sample Selection Strategies for Batch Reinforcement Learning
132. Learning Neural Contextual Bandits through Perturbed Rewards
133. Communicating via Markov Decision Processes
134. Contractive error feedback for gradient compression
135. Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents
136. Quasi-Newton policy gradient algorithms
137. Mitigating Dataset Bias Using Per-Sample Gradients From A Biased Classifier
138. Identifying Interactions among Categorical Predictors with Monte-Carlo Tree Search
139. Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets
140. AARL: Automated Auxiliary Loss for Reinforcement Learning
141. Retrieval-Augmented Reinforcement Learning
142. Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
143. Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
144. Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations
145. Dynamics-Aware Comparison of Learned Reward Functions
146. A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
147. Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
148. Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
149. Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory
150. DATA-DRIVEN EVALUATION OF TRAINING ACTION SPACE FOR REINFORCEMENT LEARNING
151. Learning Long-Term Reward Redistribution via Randomized Return Decomposition
152. Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning
153. Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems
154. CausalDyna: Improving Generalization of Dyna-style Reinforcement Learning via Counterfactual-Based Data Augmentation
155. Reasoning With Hierarchical Symbols: Reclaiming Symbolic Policies For Visual Reinforcement Learning
156. Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning
157. PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching
158. Value Gradient weighted Model-Based Reinforcement Learning
159. De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning
160. Bayesian Framework for Gradient Leakage
161. Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
162. On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
163. Rethinking Pareto Approaches in Constrained Reinforcement Learning
164. Decision Tree Algorithms for MDP
165. Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks
166. Bayesian Exploration for Lifelong Reinforcement Learning
167. Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
168. Occupy & Specify: Investigations into a Maximum Credit Assignment Occupancy Objective for Data-efficient Reinforcement Learning
169. sbfδ2-exploration for Reinforcement Learning
170. An Optics Controlling Environment and Reinforcement Learning Benchmarks
171. Learning to Annotate Part Segmentation with Gradient Matching
172. Structured Stochastic Gradient MCMC
173. Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
174. TransDreamer: Reinforcement Learning with Transformer World Models
175. Information Prioritization through Empowerment in Visual Model-based RL
176. Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent
177. Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
178. Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack
179. Continual Backprop: Stochastic Gradient Descent with Persistent Randomness
180. HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
181. TRGP: Trust Region Gradient Projection for Continual Learning
182. Ensemble Kalman Filter (EnKF) for Reinforcement Learning (RL)
183. Learning by Directional Gradient Descent
184. Revisiting Design Choices in Offline Model Based Reinforcement Learning
185. Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
186. Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management
187. SPP-RL: State Planning Policy Reinforcement Learning
188. Loss Function Learning for Domain Generalization by Implicit Gradient
189. Hypothesis Driven Coordinate Ascent for Reinforcement Learning
190. Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning
191. The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning
192. Offline Reinforcement Learning with Resource Constrained Online Deployment
193. Orchestrated Value Mapping for Reinforcement Learning
194. Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
195. Exploring unfairness in Integrated Gradients based attribution methods
196. Fourier Features in Reinforcement Learning with Neural Networks
197. COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
198. IntSGD: Adaptive Floatless Compression of Stochastic Gradients
199. Gradient Information Matters in Policy Optimization by Back-propagating through Model
200. Momentum as Variance-Reduced Stochastic Gradient
201. Optimizing Neural Networks with Gradient Lexicase Selection
202. Deep Reinforcement Learning for Equal Risk Option Pricing and Hedging under Dynamic Expectile Risk Measures
203. Depth Without the Magic: Inductive Bias of Natural Gradient Descent
204. A General Analysis of Example-Selection for Stochastic Gradient Descent
205. GrASP: Gradient-Based Affordance Selection for Planning
206. The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
207. Learning Altruistic Behaviours in Reinforcement Learning without External Rewards
208. Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?
209. Feudal Reinforcement Learning by Reading Manuals
210. Offline Reinforcement Learning with Value-based Episodic Memory
211. Pareto Navigation Gradient Descent: a First Order Algorithm for Optimization in Pareto Set
212. Avoiding Overfitting to the Importance Weights in Offline Policy Optimization
213. Gradient Imbalance and solution in Online Continual learning
214. Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
215. Fully Decentralized Model-based Policy Optimization with Networked Agents
216. Combinatorial Reinforcement Learning Based Scheduling for DNN Execution on Edge
217. Reinforcement Learning with Ex-Post Max-Min Fairness
218. Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers
219. Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
220. Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
221. Gradient flows on the feature-Gaussian manifold
222. Multi-Agent Language Learning: Symbolic Mapping
223. An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
224. EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
225. Pareto Policy Adaptation
226. Differentially Private SGD with Sparse Gradients
227. A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
228. Improving zero-shot generalization in offline reinforcement learning using generalized similarity functions
229. Improving zero-shot generalization in offline reinforcement learning using generalized similarity functions
230. Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration
231. Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
232. Learning Multi-Objective Curricula for Deep Reinforcement Learning
233. COMBO: Conservative Offline Model-Based Policy Optimization
234. Provable Hierarchy-Based Meta-Reinforcement Learning
235. The Manifold Hypothesis for Gradient-Based Explanations
236. Gradient Step Denoiser for convergent Plug-and-Play
237. Model-Based Offline Meta-Reinforcement Learning with Regularization
238. Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels
239. Sample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games
240. Causal Reinforcement Learning using Observational and Interventional Data
241. MOBA: Multi-teacher Model Based Reinforcement Learning
242. Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
243. Surprise Minimizing Multi-Agent Learning with Energy-based Models
244. Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
245. Deep Ensemble Policy Learning
246. Block Contextual MDPs for Continual Learning
247. Jointly Learning Identification and Control for Few-Shot Policy Adaptation
248. Continuous Control With Ensemble Deep Deterministic Policy Gradients
249. Safe Opponent-Exploitation Subgame Refinement
250. Understanding and Preventing Capacity Loss in Reinforcement Learning
251. Adaptive Q-learning for Interaction-Limited Reinforcement Learning
252. Reinforcement Learning in Presence of Discrete Markovian Context Evolution
253. LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning
254. Local Feature Swapping for Generalization in Reinforcement Learning
255. Orthogonalising gradients to speedup neural network optimisation
256. AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
257. Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations
258. Gradient Explosion and Representation Shrinkage in Infinite Networks
259. CoBERL: Contrastive BERT for Reinforcement Learning
260. Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
261. Adversarial Style Transfer for Robust Policy Optimization in Reinforcement Learning
262. Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models
263. Shaped Rewards Bias Emergent Language
264. Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees
265. On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
266. Knowledge is reward: Learning optimal exploration by predictive reward cashing
267. Gradient-based Counterfactual Explanations using Tractable Probabilistic Models
268. Did I do that? Blame as a means to identify controlled effects in reinforcement learning
269. Rewardless Open-Ended Learning (ROEL)
270. Offline Reinforcement Learning with In-sample Q-Learning
271. Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
272. Model-Invariant State Abstractions for Model-Based Reinforcement Learning
273. Assessing Deep Reinforcement Learning Policies via Natural Corruptions at the Edge of Imperceptibility
274. Maximizing Ensemble Diversity in Deep Reinforcement Learning
275. Theoretical understanding of adversarial reinforcement learning via mean-field optimal control
276. Auto-Encoding Inverse Reinforcement Learning
277. Multi-Agent MDP Homomorphic Networks
278. DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
279. http://deeprl.neurondance.com/d/451-iclr-2022458
280. ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
281. Mirror Descent Policy Optimization
282. Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
283. Multi-Agent Decentralized Belief Propagation on Graphs
284. On-Policy Model Errors in Reinforcement Learning
285. Learning a subspace of policies for online adaptation in Reinforcement Learning
286. Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning
287. Policy Gradients Incorporating the Future
288. Bregman Gradient Policy Optimization
289. Interacting Contour Stochastic Gradient Langevin Dynamics
290. Half-Inverse Gradients for Physical Deep Learning
291. Constrained Policy Optimization via Bayesian World Models
292. Local Patch AutoAugment with Multi-Agent Collaboration
293. Flow-based Recurrent Belief State Learning for POMDPs
294. Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
295. A Reinforcement Learning Environment for Mathematical Reasoning via Program Synthesis
296. Exploiting Minimum-Variance Policy Evaluation for Policy Optimization
297. Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient
298. Delayed Geometric Discounts: An alternative criterion for Reinforcement Learning
299. Multi-Agent Constrained Policy Optimisation
300. A Generalised Inverse Reinforcement Learning Framework
301. RL-DARTS: Differentiable Architecture Search for Reinforcement Learning
302. Representation Learning for Online and Offline RL in Low-rank MDPs
303. P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
304. Equivariant Reinforcement Learning
305. Stability and Generalisation in Batch Reinforcement Learning
306. Bolstering Stochastic Gradient Descent with Model Building
307. Efficient Wasserstein and Sinkhorn Policy Optimization
308. MURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization
309. Continual Learning with Recursive Gradient Optimization
310. Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning
311. The Information Geometry of Unsupervised Reinforcement Learning
312. On Reward Maximization and Distribution Matching for Fine-Tuning Language Models
313. Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning
314. NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
315. Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
316. Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
317. TAG: Task-based Accumulated Gradients for Lifelong learning
318. An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks
319. Learning to Shape Rewards using a Game of Two Partners
320. Transfer RL across Observation Feature Spaces via Model-Based Regularization
321. On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
322. Metrics Matter: A Closer Look on Self-Paced Reinforcement Learning
323. A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
324. Experience Replay More When It's a Key Transition in Deep Reinforcement Learning
325. Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning
326. Model-Based Robust Adaptive Semantic Segmentation
327. AQUILA: Communication Efficient Federated Learning with Adaptive Quantization of Lazily-Aggregated Gradients
328. Nested Policy Reinforcement Learning for Clinical Decision Support
329. Particle Based Stochastic Policy Optimization
330. Partial Information as Full: Reward Imputation with Sketching in Bandits
331. Personalized Heterogeneous Federated Learning with Gradient Similarity
332. Evaluating Model-Based Planning and Planner Amortization for Continuous Control
333. Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
334. SGDEM: stochastic gradient descent with energy and momentum
335. Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
336. DAAS: Differentiable Architecture and Augmentation Policy Search
337. Towards General Function Approximation in Zero-Sum Markov Games
338. Variational oracle guiding for reinforcement learning
339. HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning
340. Uncertainty Regularized Policy Learning for Offline Reinforcement Learning
341. Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
342. ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computations
343. EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning
344. DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
345. Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations
346. Accelerated Gradient-Free Method for Heavily Constrained Nonconvex Optimization
347. Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL
348. CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
349. Novel Policy Seeking with Constrained Optimization
350. Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
351. Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning
352. Reward Shifting for Optimistic Exploration and Conservative Exploitation
353. RotoGrad: Gradient Homogenization in Multitask Learning
354. Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
355. Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
356. Which model to trust: assessing the influence of models on the performance of reinforcement learning algorithms for continuous control tasks
357. Model-based Reinforcement Learning with Ensembled Model-value Expansion
358. Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
359. The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs
360. Fishr: Invariant Gradient Variances for Out-of-distribution Generalization
361. Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games?
362. POETREE: Interpretable Policy Learning with Adaptive Decision Trees
363. Imitation Learning by Reinforcement Learning
364. Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
365. Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO
366. Relative Entropy Gradient Sampler for Unnormalized Distributions
367. Sequential Communication in Multi-Agent Reinforcement Learning
368. BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks
369. ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
370. Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
371. Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
372. The Infinite Contextual Graph Markov Model
373. Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
374. Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization
375. Gradient Matching for Domain Generalization
376. Interpreting Reinforcement Policies through Local Behaviors
377. DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning
378. WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management
379. Self-Supervised Structured Representations for Deep Reinforcement Learning
380. Gradient-based Meta-solving and Its Applications to Iterative Methods for Solving Differential Equations
381. On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
382. Dropout Q-Functions for Doubly Efficient Reinforcement Learning
383. How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem
384. Meta Attention For Off-Policy Actor-Critic
385. A General Theory of Relativity in Reinforcement Learning
386. An Attempt to Model Human Trust with Reinforcement Learning
387. Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data
388. Boosted Curriculum Reinforcement Learning
389. Understanding the Generalization Gap in Visual Reinforcement Learning
390. Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel
391. Reinforcement Learning for Adaptive Mesh Refinement
392. Offline Meta-Reinforcement Learning with Online Self-Supervision
393. Gradient Importance Learning for Incomplete Observations
394. Model-Based Opponent Modeling
395. Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning
396. Hindsight Foresight Relabeling for Meta-Reinforcement Learning
397. On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime
398. Directional Bias Helps Stochastic Gradient Descent to Generalize in Nonparametric Model
399. Online Hyperparameter Meta-Learning with Hypergradient Distillation
400. FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
401. Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning
402. Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models
403. Global Convergence and Stability of Stochastic Gradient Descent
404. Learning Controllable Elements Oriented Representations for Reinforcement Learning
405. Off-Policy Reinforcement Learning with Delayed Rewards
406. TempoRL: Temporal Priors for Exploration in Off-Policy Reinforcement Learning
407. Efficient Reinforcement Learning Experimentation in PyTorch
408. Learning Synthetic Environments and Reward Networks for Reinforcement Learning
409. ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series
410. Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach
411. Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
412. Self Reward Design with Fine-grained Interpretability
413. Self Reward Design with Fine-grained Interpretability
414. MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
415. Gradient Boosting Neural Networks: GrowNet
416. Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
417. Learning Explicit Credit Assignment for Multi-agent Joint Q-learning
418. Offline Decentralized Multi-Agent Reinforcement Learning
419. Physical Gradients for Deep Learning
420. Policy improvement by planning with Gumbel
421. Batch size-invariance for policy optimization
422. Divergence-Regularized Multi-Agent Actor-Critic
423. Generalized Maximum Entropy Reinforcement Learning via Reward Shaping
424. Your value network can't evaluate every policy: A case of divergence in TD learning
425. Finite-Time Performance of Two-Timescale Networked Actor-Critic in Multi-Agent Reinforcement Learning
426. Quasi-periodic attractors carry stable gradients
427. Unsupervised fashion for controllable generations: A manifold control of latent feature using contrastive alignment and self-organizing rewards
428. Recommending Actions to Improve Engagement for Diabetes Management using Off-Policy Learning
429. Policy Advantage Networks
430. Offline Workflows for Offline Reinforcement Learning Algorithms
431. On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging
432. A Multi-Agent Koopman Operator Approach for Distributed Representation Learning of Networked Dynamical Systems
433. https://openreview.net/forum?id=IsXm6pxytcz
434. Modeling and Understanding Multi-Agent Behavior with Transformers
435. Gap-Dependent Unsupervised Exploration for Reinforcement Learning
436. Modeling the program of the world in reinforcement learning
437. Exploring Reward Surfaces in Reinforcement Learning Environments
438. TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-block Inversion
439. Eliminating Estimation Bias in Deep Deterministic Policy Gradient By Co-Regularization
440. Peering Beyond the Gradient Veil with Efficient Distributed Auto Differentiation
441. Federated Reinforcement Learning for Task Offloading in Mobile Edge Computing Networks
442. Controlling Generalization Error via Gaussian Width Gradient Complexity
443. Few-Shot Learning by Dimensionality Reduction in Gradient Space
444. Risk-sensitive Multi-agent Reinforcement Learning over adaptive networks
445. Coordinated Policy Factorization
446. What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
447. Modal-balanced training strategy by gradient adjustment and artificial Gauss noise enhancement
448. Foresight Constrained Subgoal Space for Hierarchical Reinforcement Learning via Local Imagination and Evolutionary Probing
449. RETHINKING GRADIENT WEIGHT’S INFLUENCE OVER SALIENCY MAP ESTIMATION
450. Lifelong Learning of Skills with Fast and Slow Reinforcement Learning
451. Model-Based Offline RL with Online Adaptation
452. Action-Transferable Policy Guided by Natural Language
453. Example-Based Offline Reinforcement Learning without Rewards
454. \title{Multi-Agent Reinforcement Learning Double \ Auction Design for Online Advertising Recommender Systems}
455. Research on fusion algorithm of multi-attribute decision making and reinforcement learning based on intuitionistic fuzzy number in wargame environment
456. Greedy Multi-Step Off-policy Reinforcement Learning
457. Corruption Robust Reinforcement Learning with General Function Approximation
458. Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning
原文来源：http://deeprl.neurondance.com/d/451-iclr-2022458
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