premade agent: continuous rescorla wagner

src/ActionModels.jl

@@ -14,6 +14,7 @@ export get_posteriors
 
 function __init__()
     premade_agents["binary_rw_softmax"] = premade_binary_rw_softmax
+    premade_agents["continuous_rescorla_wagner"] = premade_continuous_rescorla_wagner
 end
 
 #Types for agents and errors

src/premade_models/premade_action_models.jl

@@ -38,3 +38,35 @@ function binary_rw_softmax(agent::Agent, input::Union{Bool,Integer})
 
     return action_distribution
 end
+
+function continuous_rescorla_wagner(agent::Agent, input::Real)
+
+    ## Read in parameters from the agent
+    learning_rate = agent.parameters["learning_rate"]
+    action_noise  = agent.parameters["action_noise"]
+
+    ## Read in states with an initial value
+    old_value = agent.states["value"]
+
+    ##We dont have any settings in this model. If we had, we would read them in as well.
+    ##-----This is where the update step starts -------
+
+    ##Get new value state
+    new_value = old_value + learning_rate * (input - old_value)
+
+
+    ##-----This is where the update step ends -------
+    ##Create Bernoulli normal distribution our action probability which we calculated in the update step
+    action_distribution = Distributions.Normal(new_value, action_noise)
+
+    ##Update the states and save them to agent's history
+
+    agent.states["value"] = new_value
+    agent.states["input"] = input
+
+    push!(agent.history["value"], new_value)
+    push!(agent.history["input"], input)
+
+    ## return the action distribution to sample actions from
+    return action_distribution
+end

src/premade_models/premade_agents.jl

@@ -45,3 +45,40 @@ function premade_binary_rw_softmax(config::Dict)
         settings = settings,
     )
 end
+
+
+function premade_continuous_rescorla_wagner(config::Dict)
+
+    #Default parameters and settings
+    default_config = Dict(
+        "learning_rate" => 0.1,
+        "action_noise" => 1,
+        ("initial", "value") => 0,
+    )
+
+    #Warn the user about used defaults and misspecified keys
+    warn_premade_defaults(default_config, config)
+
+    #Merge to overwrite defaults
+    config = merge(default_config, config)
+
+    ## Create agent
+    action_model = continuous_rescorla_wagner
+    parameters = Dict(
+        "learning_rate" => config["learning_rate"],
+        "action_noise" => config["action_noise"],
+        ("initial", "value") => config[("initial", "value")],
+    )
+    states = Dict(
+        "input" => missing,
+        "value" => missing
+    )
+    settings = Dict()
+
+    return init_agent(
+        action_model,
+        parameters = parameters,
+        states = states,
+        settings = settings,
+    )
+end