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relearn-0.3.1
ReLearn: A Reinforcement Learning Library
A reinforcement learning library and experiment runner. Uses pytorch as the neural network backend via the tch interface to the C++ API.
At the moment this is designed for personal use. It is in-development and unstable so expect breaking changes with updates.
Read the documentation at https://docs.rs/relearn.
Example Runs
RL Squared Training
Replications of some results from the paper RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning by Duan et al. https://arxiv.org/pdf/1611.02779.pdf
Bandits - TRPO RL2
Set steps-per-epoch
to whatever value fits on the GPU so long as data
collection does not take more than about 15s.
By 50 epochs it should be near the optimal value of 6.6 or 6.7 (the paper shows 6.7 for UCB1 but I get 6.6).
Bandits - UCB Baseline
cargo run --release -- \
--environment meta-uniform-bernoulli-bandits \
--agent resetting-meta:ucb1 \
--num-actions 5 \
--episodes-per-trial 10 \