rl_define_new_agent: Define a New RL Agent

In jdtrat/rlsims: Simulate Reinforcement Learning Agents in R

Define a New RL Agent

Description

For users who wish to supply their own simulation code, calling this function streamlines the creation of RL Agent Objects allowing individuals to benefit from a consistent API for RL simulations and take advantage of internal safeguards.

Usage

rl_define_new_agent(
  model_type,
  simulation_code_file,
  required_args,
  required_methods = c("reinforcements", "stimuli", "arms", "policy"),
  return_agent = TRUE
)

Arguments

model_type	What type of model is the agent? E.g., "kArmedBandit" or "tdrlConditioning".
simulation_code_file	A file path containing templated simulation code. See the examples for details.
required_args	A character vector indicating required arguments of the RL Agent. For example, c("num_trials", "num_episodes", "gamma", "alpha").
required_methods	Which methods should the agent support? Setting reinforcements? Stimuli? Arms? See the examples for more details.
return_agent	Logical: TRUE (default) and the actual agent object will be returned and can be instantiated. FALSE and a string containing the code to generate the R6 Agent will be returned.

Value

If return_agent = TRUE (default), an R6 object of class model_type for simulating RL tasks with the file supplied by the simulation_code_file argument. Otherwise, a character string with the code to define the R6 object.

Examples

# Define a temporal-difference conditioning agent using the built in algorithm
td_cond_agent <- rl_define_new_agent(
  model_type = "TD Conditioning",
  # Use built in model specification "td_conditioning", setting read = FALSE to
  # simply specify the path
  simulation_code_file = use_agent_template("td_conditioning", read = FALSE),
  # What values must be set for the agent/simulation to work?
  required_args = c("num_stimuli", "num_trials", "num_episodes", "gamma", "alpha"),
  # Only need to specify reinforcements and stimuli structure
  required_methods = c("reinforcements", "stimuli"),
  return_agent = TRUE # return the actual RL Agent object
)

# Initialize a new conditioning agent as tdCond
tdCond <- td_cond_agent$new(
  model_id = "Classical Conditioning via TDRL",
  num_stimuli = 1,
  num_trials = 100,
  num_episodes = 10,
  gamma = 1,
  alpha = 0.3
  )

tdCond$
  set_stimuli(
    list(
      one = data.frame(
        onset = 3,
        offset = 8,
        magnitude = 1,
        trial = 1:100
      )
    )
  )$
  set_reinforcements(
    list(
      data.frame(
        onset = 8,
        offset = 8,
        magnitude = 1,
        trial = 1:100
      )
    )
  )$
  simulate_agent()




# Define a temporal-difference conditioning agent using the built in algorithm
td_cond_agent <- rl_define_new_agent(
  model_type = "TD Conditioning",
  # Use built in model specification "td_conditioning", setting read = FALSE to
  # simply specify the path
  simulation_code_file = use_agent_template("td_conditioning", read = FALSE),
  # What values must be set for the agent/simulation to work?
  required_args = c("num_stimuli", "num_trials", "num_episodes", "gamma", "alpha"),
  # Only need to specify reinforcements and stimuli structure
  required_methods = c("reinforcements", "stimuli"),
  return_agent = TRUE # return the actual RL Agent object
)

# Initialize a new conditioning agent as tdCond
tdCond <- td_cond_agent$new(
  model_id = "Classical Conditioning via TDRL",
  num_stimuli = 1,
  num_trials = 100,
  num_episodes = 10,
  gamma = 1,
  alpha = 0.3
)

tdCond$
  set_stimuli(
    list(
      one = data.frame(
        onset = 3,
        offset = 8,
        magnitude = 1,
        trial = 1:100
      )
    )
  )$
  set_reinforcements(
    list(
      data.frame(
        onset = 8,
        offset = 8,
        magnitude = 1,
        trial = 1:100
      )
    )
  )$
  simulate_agent()




# Define a temporal-difference conditioning agent using the built in algorithm
k_armed_agent <- rl_define_new_agent(
  model_type = "K-Armed Bandit",
  # Use built in model specification "k_armed_bandit", setting read = FALSE to
  # simply specify the path
  simulation_code_file = use_agent_template("k_armed_bandit", read = FALSE),
  # What values must be set for the agent/simulation to work?
  required_args = c(
    "num_arms", "num_trials", "num_episodes",
    "action_episode", "reinforcement_episode",
    "gamma", "alpha"
  ),
  required_methods = c("arms", "policy"), # must specify the arms and policy structure
  return_agent = TRUE # return the actual RL Agent object
)

# Initialize a k-Armed Bandit agent as 'twoArms'
twoArms <- k_armed_agent$new(
  model_id = "Two Armed Bandit Example",
  num_trials = 100,
  num_episodes = 4,
  num_arms = 2,
  action_episode = 2,
  reinforcement_episode = 3,
  gamma = 1,
  alpha = 0.3
)

# Set the arm structure, action-selection policy, and simulate
twoArms$
  set_arms(
    list(
      left = data.frame(
        probability = 0.1,
        magnitude = 1,
        alternative = 0,
        trial = 1:100
      ),
      right = data.frame(
        probability = 0.8,
        magnitude = 1,
        alternative = 0,
        trial = 1:100
      )
    )
  )$
  set_policy(
    policy = "softmax",
    tau = 0.5
  )$
  simulate_agent()

jdtrat/rlsims documentation built on March 26, 2022, 6:17 p.m.