185 citations · 340 across the 18 of their papers we have counts for
18 papers
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann +1
In this paper we explore few-shot imitation learning for control problems, which involves learning to imitate a target policy by accessing a limited set of offline rollouts. This s…
Imitating Human Behaviour with Diffusion Models
Tim Pearce, Tabish Rashid, Anssi Kanervisto +8
Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human be…
Trust-Region-Free Policy Optimization for Stochastic Policies
Mingfei Sun, Benjamin Ellis, Anuj Mahajan +3
Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies…
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
Haoxuan Pan, Deheng Ye, Xiaoming Duan +4
We revisit the estimation bias in policy gradients for the discounted episodic Markov decision process (MDP) from Deep Reinforcement Learning (DRL) perspective. The objective is fo…
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Zichuan Lin, Xiapeng Wu, Mingfei Sun +4
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 s…
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
Benjamin Ellis, Jonathan Cook, Skander Moalla +5
The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-…