Curriculum learning for reinforcement learning domains: A framework and survey
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks
in which the agent has only limited environmental feedback. Despite many advances over …
in which the agent has only limited environmental feedback. Despite many advances over …
Tunenet: One-shot residual tuning for system identification and sim-to-real robot task transfer
As researchers teach robots to perform more and more complex tasks, the need for realistic
simulation environments is growing. Existing techniques for closing the reality gap by …
simulation environments is growing. Existing techniques for closing the reality gap by …
Iterative residual tuning for system identification and sim-to-real robot learning
Robots are increasingly learning complex skills in simulation, increasing the need for
realistic simulation environments. Existing techniques for approximating real-world physics …
realistic simulation environments. Existing techniques for approximating real-world physics …
Causality and batch reinforcement learning: Complementary approaches to planning in unknown domains
J Bannon, B Windsor, W Song, T Li - arXiv preprint arXiv:2006.02579, 2020 - arxiv.org
Reinforcement learning algorithms have had tremendous successes in online learning
settings. However, these successes have relied on low-stakes interactions between the …
settings. However, these successes have relied on low-stakes interactions between the …
Optimal policy evaluation for policy optimization
S Meta - 2020 - politesi.polimi.it
Off-policy methods are the basis of a large number of effective Policy Optimization
algorithms. In this setting, Importance Sampling is typically employed as a what-if analysis …
algorithms. In this setting, Importance Sampling is typically employed as a what-if analysis …
Reinforcement Learning Architectures: SAC, TAC, and ESAC
The trend is to implement intelligent agents capable of analyzing available information and
utilize it efficiently. This work presents a number of reinforcement learning (RL) architectures; …
utilize it efficiently. This work presents a number of reinforcement learning (RL) architectures; …