Methods › Reinforcement Learning › Actor-Critic Algorithms › FORK
Forward-Looking Actor
FORK
Introduced by Honghao Wei et al. in FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
FORK, or Forward Looking Actor is a type of actor for actor-critic algorithms. In particular, FORK includes a neural network that forecasts the next state given the current state and current action, called system network; and a neural network that forecasts the reward given a (state, action) pair, called reward network. With the system network and reward network, FORK can forecast the next state and consider the value of the next state when improving the policy.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning 4 Oct 2020 · 2 repositories · arXiv:2010.01652
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| GPU | 1 |
| MuJoCo | 1 |
| Reinforcement Learning | 1 |
| Reinforcement Learning (RL) | 1 |
| reinforcement-learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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