REVIEW 1 cited by
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
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
Signed reviews
read the original abstract
Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work motivates the use of potential-based reward shaping to reduce the computational burden of each RL sub-problem. This work serves as a proof-of-concept and we hope will inspire future developments towards computationally efficient IRL.
Forward citations
Cited by 1 Pith paper
-
Bootstrapped Reward Shaping
Using the agent's current value estimate as a potential-based shaping function converges in tabular RL and accelerates DQN on Atari.
Discussion (0). Continue with ORCID to comment.