diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl
index 7935b08d..5cddbcc5 100644
--- a/src/Neuroblox.jl
+++ b/src/Neuroblox.jl
@@ -188,7 +188,7 @@ export IFNeuronBlox, LIFNeuronBlox, QIFNeuronBlox, HHNeuronExciBlox, HHNeuronInh
 export LinearNeuralMass, HarmonicOscillator, JansenRit, WilsonCowan, LarterBreakspear, NextGenerationBlox, NextGenerationResolvedBlox, NextGenerationEIBlox
 export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc
 export HebbianPlasticity, HebbianModulationPlasticity
-export Agent, ClassificationEnvironment, GreedyPolicy
+export Agent, ClassificationEnvironment, GreedyPolicy, reset!
 export LearningBlox
 export CosineSource, CosineBlox, NoisyCosineBlox, PhaseBlox, ImageStimulus
 export PowerSpectrumBlox, BandPassFilterBlox
diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl
index a6bf7a84..74d2370d 100644
--- a/src/blox/reinforcement_learning.jl
+++ b/src/blox/reinforcement_learning.jl
@@ -86,14 +86,14 @@ function maybe_set_state_post!(lr::AbstractLearningRule, state)
     end
 end
 
-mutable struct ClassificationEnvironment <: AbstractEnvironment
-    const name
-    const namespace
-    const source
-    const category
-    const N_trials
-    const t_trial
-    current_trial
+mutable struct ClassificationEnvironment{S} <: AbstractEnvironment
+    const name::Symbol
+    const namespace::Symbol
+    const source::S
+    const category::Vector{Int}
+    const N_trials::Int
+    const t_trial::Float64
+    current_trial::Int
 
     function ClassificationEnvironment(data::DataFrame; name, namespace=nothing, t_stimulus, t_pause)
         stim = ImageStimulus(
@@ -108,14 +108,14 @@ mutable struct ClassificationEnvironment <: AbstractEnvironment
         N_trials = stim.N_stimuli
         t_trial = t_stimulus + t_pause
 
-        new(name, namespace, stim, category, N_trials, t_trial, 1)
+        new{typeof(stim)}(Symbol(name), Symbol(namespace), stim, category, N_trials, t_trial, 1)
     end
 
     function ClassificationEnvironment(stim::ImageStimulus; name, namespace=nothing)
         t_trial = stim.t_stimulus + stim.t_pause
         N_trials = stim.N_stimuli
 
-        new(name, namespace, stim, stim.category, N_trials, t_trial, 1)
+        new{typeof(stim)}(Symbol(name), Symbol(namespace), stim, stim.category, N_trials, t_trial, 1)
     end
 end
 
@@ -123,6 +123,8 @@ end
 
 increment_trial!(env::AbstractEnvironment) = env.current_trial += 1
 
+reset!(env::AbstractEnvironment) = env.current_trial = 1
+
 function get_trial_stimulus(env::ClassificationEnvironment)
     stim_params = env.source.stim_parameters
     stim_values = env.source.IMG[:, env.current_trial]
@@ -133,11 +135,11 @@ end
 abstract type AbstractActionSelection <: AbstractBlox end
 
 mutable struct GreedyPolicy <: AbstractActionSelection
-    const name
-    const namespace
-    competitor_states 
-    competitor_params
-    const t_decision
+    const name::Symbol
+    const namespace::Symbol
+    competitor_states::Vector{Num}
+    competitor_params::Vector{Num}
+    const t_decision::Float64
 
     function GreedyPolicy(; name, t_decision, namespace=nothing, competitor_states=nothing, competitor_params=nothing)
         sts = isnothing(competitor_states) ? Num[] : competitor_states
@@ -150,6 +152,7 @@ function (p::GreedyPolicy)(sol::SciMLBase.AbstractSciMLSolution)
     comp_vals = sol(p.t_decision; idxs=p.competitor_states)
     return argmax(comp_vals)
 end
+
 """
 function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem)
     ps = parameters(sys)
@@ -166,11 +169,13 @@ function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem)
     return argmax(comp_vals)
 end
 """
-mutable struct Agent 
-    odesystem
-    problem
-    action_selection
-    learning_rules
+
+mutable struct Agent{S,P,A,LR}
+    odesystem::S
+    problem::P
+    action_selection::A
+    learning_rules::LR
+    init_params::Vector{Float64}
 
     function Agent(g::MetaDiGraph; name, kwargs...)
         bc = connector_from_graph(g)
@@ -183,16 +188,19 @@ mutable struct Agent
         u0 = haskey(kwargs, :u0) ? kwargs[:u0] : []
         p = haskey(kwargs, :p) ? kwargs[:p] : []
         
-        prob = ODEProblem(ss, u0, (0,1), p)
-
+        prob = ODEProblem(ss, u0, (0.,1.), p)
+        init_params = prob.p
+        
         policy = action_selection_from_graph(g)
         learning_rules = bc.learning_rules
 
-        new(ss, prob, policy, learning_rules)
+        
+
+        new{typeof(sys), typeof(prob), typeof(policy), typeof(learning_rules)}(ss, prob, policy, learning_rules, init_params)
     end
 end
 
-
+reset!(ag::Agent) = ag.problem = remake(ag.problem; p = ag.init_params)
 
 function run_experiment!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
     N_trials = env.N_trials
diff --git a/test/reinforcement_learning.jl b/test/reinforcement_learning.jl
index 25e0e915..39fee2eb 100644
--- a/test/reinforcement_learning.jl
+++ b/test/reinforcement_learning.jl
@@ -69,4 +69,10 @@ using CSV
     @test any(init_params[map_idxs[idxs_weight]] .!= final_params[map_idxs[idxs_weight]])
     # All non-weight parameters need to be the same.
     @test all(init_params[map_idxs[idxs_other_params]] .== final_params[map_idxs[idxs_other_params]])
+
+    reset!(agent)
+    @test all(init_params .== agent.problem.p) 
+
+    reset!(env)
+    @test env.current_trial == 1
 end