Search Results for author:
Found 26 papers, 10 papers with code
Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games
In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore.
$TAR^2$: Temporal-Agent Reward Redistribution for Optimal Policy Preservation in Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning (MARL), learning effective policies is challenging when global rewards are sparse and delayed.
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to evaluate actions at intermediate time steps.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models
Training large models with millions or even billions of parameters from scratch incurs substantial computational costs.
Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion
We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89. 7 \%) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi.
FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation
Preference-based learning aims to align robot task objectives with human values.
TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions
Our "plug-and-play" method incorporates a top-down decoder to a pre-trained navigation model.
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.
Few-Shot Image Classification
Few-Shot Imitation Learning
+4
Trust-Region-Free Policy Optimization for Stochastic Policies
In this paper, we show that the trust region constraint over policies can be safely substituted by a trust-region-free constraint without compromising the underlying monotonic improvement guarantee.
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for sample reuse.
Imitating Human Behaviour with Diffusion Models
This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments.
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
We show that, despite such state distribution shift, the policy gradient estimation bias can be reduced in the following three ways: 1) a small learning rate; 2) an adaptive-learning-rate-based optimizer; and 3) KL regularization.
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
In this work, we conduct new analysis demonstrating that SMAC lacks the stochasticity and partial observability to require complex *closed-loop* policies.
UniMASK: Unified Inference in Sequential Decision Problems
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
Trust Region Bounds for Decentralized PPO Under Non-stationarity
We present trust region bounds for optimizing decentralized policies in cooperative Multi-Agent Reinforcement Learning (MARL), which holds even when the transition dynamics are non-stationary.
Generalization in Cooperative Multi-Agent Systems
Specifically, we study generalization bounds under a linear dependence of the underlying dynamics on the agent capabilities, which can be seen as a generalization of Successor Features to MAS.
You May Not Need Ratio Clipping in PPO
Furthermore, we show that ESPO can be easily scaled up to distributed training with many workers, delivering strong performance as well.
Birds Eye View Social Distancing Analysis System
We propose and evaluate a privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections.
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency
Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications.
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
We present SoftDICE, which achieves state-of-the-art performance for imitation learning.
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games.
Adversarial Imitation Learning from Incomplete Demonstrations
In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a policy from demonstrations with incomplete action sequences, i. e., incomplete demonstrations.
