reinforcelearn (version 0.2.1)

makeAgent: Create Agent.

Description

An agent consists of a policy and (optional) a value function representation and (optional) a learning algorithm.

Usage

makeAgent(policy, val.fun = NULL, algorithm = NULL,
  preprocess = identity, replay.memory = NULL, policy.args = list(),
  val.fun.args = list(), algorithm.args = list())

Arguments

policy

[character(1) | Policy] A policy. If you pass a string the policy will be created via makePolicy.

val.fun

[character(1) | ValueFunction] A value function representation. If you pass a string the value function will be created via makeValueFunction.

algorithm

[character(1) | Algorithm] An algorithm. If you pass a string the algorithm will be created via makeAlgorithm.

preprocess

[function] A function which preprocesses the state so that the agent can learn on this.

replay.memory

[ReplayMemory] Replay memory for experience replay created by makeReplayMemory.

policy.args

[list] Arguments passed on to args in makePolicy.

val.fun.args

[list] Arguments passed on to args in makeValueFunction.

algorithm.args

[list] Arguments passed on to args in makeAlgorithm.

Examples

# NOT RUN {
agent = makeAgent("softmax", "table", "qlearning")
# }