GradientPolicy is a SoftMax type algorithm, based on Sutton & Barton (2018).
alpha = 0.1
double, temperature parameter alpha specifies how many arms we can explore. When alpha is high, all arms are explored equally, when alpha is low, arms offering higher rewards will be chosen.
new(epsilon = 0.1)
Generates a new
GradientPolicy object. Arguments are defined in
the Argument section above.
each policy needs to assign the parameters it wants to keep track of
self$theta_to_arms that has to be defined in
The parameters defined here can later be accessed by arm index in the following way:
here, a policy decides which arm to choose, based on the current values of its parameters and, potentially, the current context.
set_reward(reward, context), a policy updates its parameter values
based on the reward received, and, potentially, the current context.
Kuleshov, V., & Precup, D. (2014). Algorithms for multi-armed bandit problems. arXiv preprint arXiv:1402.6028.
Cesa-Bianchi, N., Gentile, C., Lugosi, G., & Neu, G. (2017). Boltzmann exploration done right. In Advances in Neural Information Processing Systems (pp. 6284-6293).
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horizon <- 100L simulations <- 100L weights <- c(0.9, 0.1, 0.1) policy <- GradientPolicy$new(alpha = 0.1) bandit <- BasicBernoulliBandit$new(weights = weights) agent <- Agent$new(policy, bandit) history <- Simulator$new(agent, horizon, simulations, do_parallel = FALSE)$run() plot(history, type = "cumulative") plot(history, type = "arms")
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