computePolicy: Computes the reinforcement learning policy
In nproellochs/ReinforcementLearning: Model-Free Reinforcement Learning

Description Usage Arguments Value See Also Examples

Computes reinforcement learning policy from a given state-action table Q. The policy is the decision-making function of the agent and defines the learning agent's behavior at a given time.

1	computePolicy(x)

`x`	Variable which encodes the behavior of the agent. This can be either a `matrix`, `data.frame` or an `rl` object.

Returns the learned policy.

ReinforcementLearning

# Create exemplary state-action table (Q) with 2 actions and 3 states
Q <- data.frame("up" = c(-1, 0, 1), "down" = c(-1, 1, 0))

# Show best possible action in each state
computePolicy(Q)