ReinforcementLearning
Model-Free Reinforcement Learning
Performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.5 |
rolling linux/jammy R-4.5 | ReinforcementLearning_1.0.5.tar.gz |
2.0 MiB |
1.0.5 |
rolling linux/noble R-4.5 | ReinforcementLearning_1.0.5.tar.gz |
2.0 MiB |
1.0.5 |
rolling source/ R- | ReinforcementLearning_1.0.5.tar.gz |
910.4 KiB |
1.0.5 |
latest linux/jammy R-4.5 | ReinforcementLearning_1.0.5.tar.gz |
2.0 MiB |
1.0.5 |
latest linux/noble R-4.5 | ReinforcementLearning_1.0.5.tar.gz |
2.0 MiB |
1.0.5 |
latest source/ R- | ReinforcementLearning_1.0.5.tar.gz |
910.4 KiB |
1.0.5 |
2026-04-26 source/ R- | ReinforcementLearning_1.0.5.tar.gz |
910.4 KiB |
1.0.5 |
2026-04-23 source/ R- | ReinforcementLearning_1.0.5.tar.gz |
910.4 KiB |
1.0.5 |
2026-04-09 windows/windows R-4.5 | ReinforcementLearning_1.0.5.zip |
2.1 MiB |
1.0.5 |
2025-04-20 source/ R- | ReinforcementLearning_1.0.5.tar.gz |
910.4 KiB |