Search Results for author: Akash Velu

Found 5 papers, 3 papers with code

Differentiable Weight Masks for Domain Transfer

no code implementations26 Aug 2023 Samar Khanna, Skanda Vaidyanath, Akash Velu

For instance, given a network that has been trained on a source task, we would like to re-train this network on a similar, yet different, target task while maintaining its performance on the source task.

Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning

1 code implementation21 Jul 2023 Akash Velu, Skanda Vaidyanath, Dilip Arumugam

Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents.

Decision Making Off-policy evaluation +2

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments

1 code implementation31 Dec 2021 Abhiram Iyer, Karan Grewal, Akash Velu, Lucas Oliveira Souza, Jeremy Forest, Subutai Ahmad

Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training.

Continual Learning Multi-Task Learning

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

13 code implementations2 Mar 2021 Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, Yi Wu

This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.

Multi-agent Reinforcement Learning reinforcement-learning +3

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms

no code implementations1 Jan 2021 Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu

We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.

Benchmarking reinforcement-learning +2

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