REVIEW 4 cited by
imitation: Clean Imitation Learning Implementations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code. Moreover, the algorithms are implemented in a modular fashion, making it simple to develop novel algorithms in the framework. Our source code, including documentation and examples, is available at https://github.com/HumanCompatibleAI/imitation
Forward citations
Cited by 4 Pith papers
-
Understanding electricity consumption behaviour through Inverse Reinforcement Learning
AIRL-recovered reward functions show that the 2022 energy crisis and heatwave reshaped Italian households' cooling responses heterogeneously, sometimes durably, with time-of-use as an independent dimension.
-
Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making
MAYA reproduces individual bee left/right choices by matching regret trajectories to four bandit policies with a memory window fixed at tau=7, but the tau value and best metric are selected on the same data used for e...
-
Learning Dolly-In Filming From Demonstration Using a Ground-Based Robot
A GAIL-based imitation learning pipeline trained on 25 joystick demonstrations produces dolly-in shots that transfer from simulation to a real ground robot and outperform a PPO baseline.
-
Price Aware Power Split Control in Heterogeneous Battery Storage Systems
A single-stage framework coupling battery dispatch with internal power splitting shows LP maximizes savings and SOC balance while RL improves efficiency and thermal balance.
Discussion (0). Continue with ORCID to comment.