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Fri Dec 09 08:25 AM -- 05:35 PM (PST) @ Virtual None
Deep Reinforcement Learning Workshop
Karol Hausman · Qi Zhang · Matthew Taylor · Martha White · Suraj Nair · Manan Tomar · Risto Vuorio · Ted Xiao · Zeyu Zheng · Manan Tomar

Workshop Home Page

In recent years, the use of deep neural networks as function approximators has enabled researchers to extend reinforcement learning techniques to solve increasingly complex control tasks. The emerging field of deep reinforcement learning has led to remarkable empirical results in rich and varied domains like robotics, strategy games, and multi-agent interactions. This workshop will bring together researchers working at the intersection of deep learning and reinforcement learning, and it will help interested researchers outside of the field gain a high-level view about the current state of the art and potential directions for future contributions.

Opening Remarks
Tobias Gerstenberg (Invited Talk)
Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training (Poster)
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function (Poster)
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes (Poster)
Jakob Foerster (Invited Talk)
Scientific Experiments in Reinforcement Learning (Opinion Talk)
Transformers are Sample-Efficient World Models (Poster)
Scaling Laws for a Multi-Agent Reinforcement Learning Model (Poster)
Natasha Jaques (Opinion Talk)
The World is not Uniformly Distributed; Important Implications for Deep RL (Opinion Talk)
Amy Zhang (Invited Talk)
Igor Mordatch (Invited Talk)
John Schulman (Implementation Talk)
Danijar Hafner (Implementation Talk)
Kristian Hartikainen (Implementation Talk)
Ilya Kostrikov, Aviral Kumar (Implementation Talk)
Panel Discussion
Closing Remarks
What Makes Certain Pre-Trained Visual Representations Better for Robotic Learning? (Poster)
Curiosity in Hindsight (Poster)
Train Offline, Test Online: A Real Robot Learning Benchmark (Poster)
A Framework for Predictable Actor-Critic Control (Poster)
Ensemble based uncertainty estimation with overlapping alternative predictions (Poster)
Offline Reinforcement Learning on Real Robot with Realistic Data Sources (Poster)
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments (Poster)
Training Equilibria in Reinforcement Learning (Poster)
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games (Poster)
Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning (Poster)
Confidence-Conditioned Value Functions for Offline Reinforcement Learning (Poster)
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance (Poster)
Integrating Episodic and Global Bonuses for Efficient Exploration (Poster)
Deconfounded Imitation Learning (Poster)
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning (Poster)
Human-AI Coordination via Human-Regularized Search and Learning (Poster)
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks (Poster)
Return Augmentation gives Supervised RL Temporal Compositionality (Poster)
Design Process is a Reinforcement Learning Problem (Poster)
Bayesian Q-learning With Imperfect Expert Demonstrations (Poster)
Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting (Poster)
Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning (Poster)
Offline Reinforcement Learning from Heteroskedastic Data Via Support Constraints (Poster)
Variance Double-Down: The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning (Poster)
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems (Poster)
In the ZONE: Measuring difficulty and progression in curriculum generation (Poster)
Better state exploration using action sequence equivalence (Poster)
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment (Poster)
Guiding Exploration Towards Impactful Actions (Poster)
Domain Invariant Q-Learning for model-free robust continuous control under visual distractions (Poster)
Multi-Agent Policy Transfer via Task Relationship Modeling (Poster)
Foundation Models for History Compression in Reinforcement Learning (Poster)
A Game-Theoretic Perspective of Generalization in Reinforcement Learning (Poster)
Imitating Human Behaviour with Diffusion Models (Poster)
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model (Poster)
ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation (Poster)
Quantization-aware Policy Distillation (QPD) (Poster)
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search (Poster)
Cyclophobic Reinforcement Learning (Poster)
AsymQ: Asymmetric Q-loss to mitigate overestimation bias in off-policy reinforcement learning (Poster)
Fine-tuning Offline Policies with Optimistic Action Selection (Poster)
SEM2: Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model (Poster)
Policy Architectures for Compositional Generalization in Control (Poster)
Rethinking Learning Dynamics in RL using Adversarial Networks (Poster)
Look Back When Surprised: Stabilizing Reverse Experience Replay for Neural Approximation (Poster)
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction (Poster)
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization (Poster)
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier (Poster)
Adversarial Policies Beat Professional-Level Go AIs (Poster)
Efficient Multi-Task Reinforcement Learning via Selective Behavior Sharing (Poster)
Contrastive Example-Based Control (Poster)
A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations (Poster)
Multi-skill Mobile Manipulation for Object Rearrangement (Poster)
Visual Reinforcement Learning with Self-Supervised 3D Representations (Poster)
One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentation (Poster)
Building a Subspace of Policies for Scalable Continual Learning (Poster)
Skill Machines: Temporal Logic Composition in Reinforcement Learning (Poster)
Learning Representations for Reinforcement Learning with Hierarchical Forward Models (Poster)
In-context Reinforcement Learning with Algorithm Distillation (Poster)
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm (Poster)
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations (Poster)
Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation (Poster)
Graph Inverse Reinforcement Learning from Diverse Videos (Poster)
Simple Emergent Action Representations from Multi-Task Policy Training (Poster)
Adversarial Cheap Talk (Poster)
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning (Poster)
SPRINT: Scalable Semantic Policy Pre-training via Language Instruction Relabeling (Poster)
Towards True Lossless Sparse Communication in Multi-Agent Systems (Poster)
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning (Poster)
PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots (Poster)
Offline Reinforcement Learning for Customizable Visual Navigation (Poster)
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning (Poster)
Hyperbolic Deep Reinforcement Learning (Poster)
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning (Poster)
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning (Poster)
Efficient Exploration using Model-Based Quality-Diversity with Gradients (Poster)
Choreographer: Learning and Adapting Skills in Imagination (Poster)
Giving Robots a Hand: Broadening Generalization via Hand-Centric Human Video Demonstrations (Poster)
Efficient Offline Policy Optimization with a Learned Model (Poster)
Emergent collective intelligence from massive-agent cooperation and competition (Poster)
Distance-Sensitive Offline Reinforcement Learning (Poster)
Uncertainty-Driven Exploration for Generalization in Reinforcement Learning (Poster)
Language Models Can Teach Themselves to Program Better (Poster)
Graph Q-Learning for Combinatorial Optimization (Poster)
Transformer-based World Models Are Happy With 100k Interactions (Poster)
Contrastive Value Learning: Implicit Models for Simple Offline RL (Poster)
CASA: Bridging the Gap between Policy Improvement and Policy Evaluation with Conflict Averse Policy Iteration (Poster)
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning (Poster)
Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning (Poster)
Evaluating Long-Term Memory in 3D Mazes (Poster)
Visual Imitation Learning with Patch Rewards (Poster)
Memory-Efficient Reinforcement Learning with Priority based on Surprise and On-policyness (Poster)
Learning a Domain-Agnostic Policy through Adversarial Representation Matching for Cross-Domain Policy Transfer (Poster)
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning (Poster)
Toward Effective Deep Reinforcement Learning for 3D Robotic Manipulation: End-to-End Learning from Multimodal Raw Sensory Data (Poster)
Momentum Boosted Episodic Memory for Improving Learning in Long-Tailed RL Environments (Poster)
A Ranking Game for Imitation Learning (Poster)
Implicit Offline Reinforcement Learning via Supervised Learning (Poster)
Distributional deep Q-learning with CVaR regression (Poster)
The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning (Poster)
Understanding Hindsight Goal Relabeling Requires Rethinking Divergence Minimization (Poster)
Perturbed Quantile Regression for Distributional Reinforcement Learning (Poster)
Concept-based Understanding of Emergent Multi-Agent Behavior (Poster)
Constrained Imitation Q-learning with Earth Mover’s Distance reward (Poster)
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement (Poster)
SoftTreeMax: Policy Gradient with Tree Search (Poster)
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation (Poster)
Hypernetwork-PPO for Continual Reinforcement Learning (Poster)
DRL-EPANET: Deep reinforcement learning for optimal control at scale in Water Distribution Systems (Poster)
Actor Prioritized Experience Replay (Poster)
Model and Method: Training-Time Attack for Cooperative Multi-Agent Reinforcement Learning (Poster)
Converging to Unexploitable Policies in Continuous Control Adversarial Games (Poster)
Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning (Poster)
On All-Action Policy Gradients (Poster)
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning (Poster)
The Benefits of Model-Based Generalization in Reinforcement Learning (Poster)
Training graph neural networks with policy gradients to perform tree search (Poster)
Co-Imitation: Learning Design and Behaviour by Imitation (Poster)
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning (Poster)
BLaDE: Robust Exploration via Diffusion Models (Poster)
Learning Semantics-Aware Locomotion Skills from Human Demonstrations (Poster)
Imitation from Observation With Bootstrapped Contrastive Learning (Poster)
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm (Poster)
Improving Assistive Robotics with Deep Reinforcement Learning (Poster)
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning (Poster)
Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning (Poster)
Variance Reduction in Off-Policy Deep Reinforcement Learning using Spectral Normalization (Poster)
Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents (Poster)
Guided Skill Learning and Abstraction for Long-Horizon Manipulation (Poster)
Locally Constrained Representations in Reinforcement Learning (Poster)
Sample-efficient Adversarial Imitation Learning (Poster)
Prioritizing Samples in Reinforcement Learning with Reducible Loss (Poster)
PCRL: Priority Convention Reinforcement Learning for Microscopically Sequencable Multi-agent Problems (Poster)
Compositional Task Generalization with Modular Successor Feature Approximators (Poster)
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective (Poster)
Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition (Poster)
Reinforcement Learning in System Identification (Poster)
Robust Option Learning for Adversarial Generalization (Poster)
Biological Neurons vs Deep Reinforcement Learning: Sample efficiency in a simulated game-world (Poster)
Inducing Functions through Reinforcement Learning without Task Specification (Poster)
Learning Successor Feature Representations to Train Robust Policies for Multi-task Learning (Poster)
Automated Dynamics Curriculums for Deep Reinforcement Learning (Poster)
Supervised Q-Learning for Continuous Control (Poster)
MOPA: a Minimalist Off-Policy Approach to Safe-RL (Poster)
Novel Policy Seeking with Constrained Optimization (Poster)
Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference (Poster)
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning (Poster)
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps (Poster)
Neural All-Pairs Shortest Path for Reinforcement Learning (Poster)
VI2N: A Network for Planning Under Uncertainty based on Value of Information (Poster)
Efficient Multi-Horizon Learning for Off-Policy Reinforcement Learning (Poster)
Analyzing the Sensitivity to Policy-Value Decoupling in Deep Reinforcement Learning Generalization (Poster)
Lagrangian Model Based Reinforcement Learning (Poster)
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines (Poster)
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes (Poster)
Informative rewards and generalization in curriculum learning (Poster)
Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation (Poster)
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning (Poster)
The Emphatic Approach to Average-Reward Policy Evaluation (Poster)
Learning Exploration Policies with View-based Intrinsic Rewards (Poster)
Scaling Covariance Matrix Adaptation MAP-Annealing to High-Dimensional Controllers (Poster)
Policy Aware Model Learning via Transition Occupancy Matching (Poster)
On The Fragility of Learned Reward Functions (Poster)
Temporary Goals for Exploration (Poster)
Revisiting Bellman Errors for Offline Model Selection (Poster)
Unleashing The Potential of Data Sharing in Ensemble Deep Reinforcement Learning (Poster)