7 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
Muthukumar Pandaram, Jakob Hollenstein, David Drexel +3
The use of learned dynamics models, also known as world models, can improve the sample efficiency of reinforcement learning. Recent work suggests that the underlying causal graphs…
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo +3
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as settin…
A Temporal-Difference Approach to Policy Gradient Estimation
Samuele Tosatto, Andrew Patterson, Martha White +1
The policy gradient theorem (Sutton et al., 2000) prescribes the usage of a cumulative discounted state distribution under the target policy to approximate the gradient. Most algor…
Model-free Policy Learning with Reward Gradients
Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi +1
Despite the increasing popularity of policy gradient methods, they are yet to be widely utilized in sample-scarce applications, such as robotics. The sample efficiency could be imp…
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, João Carvalho, Jan Peters
Off-policy Reinforcement Learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current…
A Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, Joao Carvalho, Hany Abdulsamad +1
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is es…