Restructured code to include all environments

src/RL/LocalCOMEnv.jl

@@ -0,0 +1,140 @@
+export LocalCOMEnv
+
+struct LocalCOMEnv <: Env
+    params::EnvParams
+
+    distance_state_space::Vector{Interval}
+    direction_angle_state_space::Vector{Interval}
+
+    max_distance::Float64
+
+    function LocalCOMEnv(
+        sim_consts; n_distance_states::Int64=3, n_direction_angle_states::Int64=3
+    )
+        @assert n_direction_angle_states > 1
+
+        direction_angle_state_space = gen_angle_state_space(n_direction_angle_states)
+
+        min_distance = 0.0
+        max_distance = sim_consts.skin_r
+
+        distance_state_space = gen_distance_state_space(
+            min_distance, max_distance, n_distance_states
+        )
+
+        n_states = n_distance_states * n_direction_angle_states + 1
+
+        state_space = Vector{SVector{2,Interval}}(undef, n_states - 1)
+
+        ind = 1
+        for distance_state in distance_state_space
+            for direction_angle_state in direction_angle_state_space
+                state_space[ind] = SVector(distance_state, direction_angle_state)
+                ind += 1
+            end
+        end
+        # Last state is when no particle is in the skin radius
+
+        params = EnvParams(n_states, state_space)
+
+        return new(params, distance_state_space, direction_angle_state_space, max_distance)
+    end
+end
+
+struct LocalCOMEnvHelper <: EnvHelper
+    params::EnvHelperParams
+
+    vec_to_neighbour_sums::Vector{SVector{2,Float64}}
+    n_neighbours::Vector{Int64}
+
+    function LocalCOMEnvHelper(params::EnvHelperParams)
+        return new(
+            params, fill(SVector(0.0, 0.0), params.n_particles), fill(0, params.n_particles)
+        )
+    end
+end
+
+function gen_env_helper(::LocalCOMEnv, env_helper_params::EnvHelperParams)
+    return LocalCOMEnvHelper(env_helper_params)
+end
+
+function pre_integration_hook(env_helper::LocalCOMEnvHelper)
+    @simd for id in 1:(env_helper.params.n_particles)
+        env_helper.vec_to_neighbour_sums[id] = SVector(0.0, 0.0)
+        env_helper.n_neighbours[id] = 0
+    end
+
+    return nothing
+end
+
+function state_update_helper_hook(
+    env_helper::LocalCOMEnvHelper, id1::Int64, id2::Int64, r⃗₁₂::SVector{2,Float64}
+)
+    env_helper.vec_to_neighbour_sums[id1] += r⃗₁₂
+    env_helper.vec_to_neighbour_sums[id2] -= r⃗₁₂
+
+    env_helper.n_neighbours[id1] += 1
+    env_helper.n_neighbours[id2] += 1
+
+    return nothing
+end
+
+function state_update_hook(env_helper::LocalCOMEnvHelper, particles::Vector{Particle})
+    n_particles = env_helper.params.n_particles
+
+    @turbo for id in 1:(n_particles)
+        env_helper.params.old_states_ind[id] = env_helper.params.states_ind[id]
+    end
+
+    env = env_helper.params.env
+
+    for id in 1:n_particles
+        n_neighbours = env_helper.n_neighbours[id]
+
+        if n_neighbours == 0
+            state_ind = env.params.n_states
+        else
+            vec_to_local_center_of_mass =
+                env_helper.vec_to_neighbour_sums[id] / n_neighbours
+
+            distance = sqrt(
+                vec_to_local_center_of_mass[1]^2 + vec_to_local_center_of_mass[2]^2
+            )
+
+            distance_state = find_state_interval(distance, env.distance_state_space)
+
+            si, co = sincos(particles[id].φ)
+
+            direction_angle = angle2(SVector(co, si), vec_to_local_center_of_mass)
+
+            direction_angle_state = find_state_interval(
+                direction_angle, env.direction_angle_state_space
+            )
+
+            state = SVector{2,Interval}(distance_state, direction_angle_state)
+            state_ind = find_state_ind(state, env.params.state_space)
+        end
+
+        env_helper.params.states_ind[id] = state_ind
+    end
+
+    return nothing
+end
+
+function update_reward!(env::LocalCOMEnv, env_helper::LocalCOMEnvHelper, particle::Particle)
+    id = particle.id
+
+    normalization = (env.max_distance * env_helper.params.n_particles)
+
+    n_neighbours = env_helper.n_neighbours[id]
+    if n_neighbours == 0
+        env.params.reward = -(env.max_distance^2) / normalization
+    else
+        vec_to_local_center_of_mass = env_helper.vec_to_neighbour_sums[id] / n_neighbours # TODO: Reuse vec_to_local_center_of_mass from state_update_hook
+        env.params.reward =
+            -(vec_to_local_center_of_mass[1]^2 + vec_to_local_center_of_mass[2]^2) /
+            normalization
+    end
+
+    return nothing
+end

src/RL/RL.jl

@@ -1,6 +1,6 @@
 module RL
 
-export run_rl
+export run_rl, LocalCOMEnv
 
 using Base: OneTo
 
@@ -12,12 +12,14 @@ using LoopVectorization: @turbo
 using Random: Random
 using ProgressMeter: @showprogress
 
-using ..ReCo: ReCo, Particle, angle2, Shape
+using ..ReCo: ReCo, Particle, angle2, Shape, DEFAULT_SKIN_TO_INTERACTION_R_RATIO
 
-const INITIAL_REWARD = 0.0
 const INITIAL_STATE_IND = 1
+const INITIAL_REWARD = 0.0
 
-function angle_state_space(n_angle_states::Int64)
+method_not_implemented() = error("Method not implemented!")
+
+function gen_angle_state_space(n_angle_states::Int64)
     angle_range = range(; start=-π, stop=π, length=n_angle_states + 1)
 
     angle_state_space = Vector{Interval}(undef, n_angle_states)
@@ -37,57 +39,12 @@ function angle_state_space(n_angle_states::Int64)
     return angle_state_space
 end
 
-mutable struct Env <: AbstractEnv
+function gen_distance_state_space(
-    n_actions::Int64
+    min_distance::Float64, max_distance::Float64, n_distance_states::Int64
-    action_space::Vector{SVector{2,Float64}}
+)
-    action_ind_space::OneTo{Int64}
-
-    distance_state_space::Vector{Interval}
-    direction_angle_state_space::Vector{Interval}
-
-    n_states::Int64
-    state_space::Vector{SVector{2,Interval}}
-    state_ind_space::OneTo{Int64}
-    state_ind::Int64
-
-    reward::Float64
-    terminated::Bool
-
-    function Env(;
-        max_distance::Float64,
-        min_distance::Float64=0.0,
-        n_v_actions::Int64=2,
-        n_ω_actions::Int64=3,
-        max_v::Float64=40.0,
-        max_ω::Float64=π / 2,
-        n_distance_states::Int64=3,
-        n_direction_angle_states::Int64=3,
-    )
     @assert min_distance >= 0.0
     @assert max_distance > min_distance
-        @assert n_v_actions > 1
-        @assert n_ω_actions > 1
-        @assert max_v > 0
-        @assert max_ω > 0
     @assert n_distance_states > 1
-        @assert n_direction_angle_states > 1
-
-        v_action_space = range(; start=0.0, stop=max_v, length=n_v_actions)
-        ω_action_space = range(; start=-max_ω, stop=max_ω, length=n_ω_actions)
-
-        n_actions = n_v_actions * n_ω_actions
-
-        action_space = Vector{SVector{2,Float64}}(undef, n_actions)
-
-        ind = 1
-        for v in v_action_space
-            for ω in ω_action_space
-                action_space[ind] = SVector(v, ω)
-                ind += 1
-            end
-        end
-
-        action_ind_space = OneTo(n_actions)
 
     distance_range = range(;
         start=min_distance, stop=max_distance, length=n_distance_states + 1
@@ -107,29 +64,60 @@ mutable struct Env <: AbstractEnv
         )
     end
 
-        direction_angle_state_space = angle_state_space(n_direction_angle_states)
+    return distance_state_space
+end
 
-        n_states = n_distance_states * n_direction_angle_states + 1
+abstract type Env <: AbstractEnv end
 
-        state_space = Vector{SVector{2,Interval}}(undef, n_states - 1)
+mutable struct EnvParams{state_dims}
+    n_actions::Int64
+    action_space::Vector{SVector{2,Float64}}
+    action_ind_space::OneTo{Int64}
+
+    n_states::Int64
+    state_space::Vector{SVector{state_dims,Interval}}
+    state_ind_space::OneTo{Int64}
+    state_ind::Int64
+
+    reward::Float64
+    terminated::Bool
+
+    function EnvParams(
+        n_states::Int64,
+        state_space::Vector{SVector{state_dims,Interval}};
+        n_v_actions::Int64=2,
+        n_ω_actions::Int64=3,
+        max_v::Float64=40.0,
+        max_ω::Float64=π / 2,
+    ) where {state_dims}
+        @assert n_v_actions > 1
+        @assert n_ω_actions > 1
+        @assert max_v > 0
+        @assert max_ω > 0
+
+        v_action_space = range(; start=0.0, stop=max_v, length=n_v_actions)
+        ω_action_space = range(; start=-max_ω, stop=max_ω, length=n_ω_actions)
+
+        n_actions = n_v_actions * n_ω_actions
+
+        action_space = Vector{SVector{2,Float64}}(undef, n_actions)
 
         ind = 1
-        for distance_state in distance_state_space
+        for v in v_action_space
-            for direction_angle_state in direction_angle_state_space
+            for ω in ω_action_space
-                state_space[ind] = SVector(distance_state, direction_angle_state)
+                action_space[ind] = SVector(v, ω)
                 ind += 1
             end
         end
-        # Last state is when no particle is in the skin radius
+
+        action_ind_space = OneTo(n_actions)
 
         state_ind_space = OneTo(n_states)
 
-        return new(
+        return new{state_dims}(
             n_actions,
             action_space,
             action_ind_space,
-            distance_state_space,
-            direction_angle_state_space,
             n_states,
             state_space,
             state_ind_space,
@@ -141,94 +129,78 @@ mutable struct Env <: AbstractEnv
 end
 
 function reset!(env::Env)
-    env.state_ind = env.n_states
+    env.params.terminated = false
-    env.terminated = false
 
     return nothing
 end
 
-RLBase.state_space(env::Env) = env.state_ind_space
+RLBase.state_space(env::Env) = env.params.state_ind_space
 
-RLBase.state(env::Env) = env.state_ind
+RLBase.state(env::Env) = env.params.state_ind
 
-RLBase.action_space(env::Env) = env.action_ind_space
+RLBase.action_space(env::Env) = env.params.action_ind_space
 
-RLBase.reward(env::Env) = env.reward
+RLBase.reward(env::Env) = env.params.reward
 
-RLBase.is_terminated(env::Env) = env.terminated
+RLBase.is_terminated(env::Env) = env.params.terminated
 
-struct Params{H<:AbstractHook}
+struct EnvHelperParams{H<:AbstractHook}
     env::Env
     agent::Agent
     hook::H
 
+    n_steps_before_actions_update::Int64
+
+    goal_gyration_tensor_eigvals_ratio::Float64
+
+    n_particles::Int64
+
     old_states_ind::Vector{Int64}
     states_ind::Vector{Int64}
 
     actions::Vector{SVector{2,Float64}}
     actions_ind::Vector{Int64}
 
-    n_steps_before_actions_update::Int64
+    function EnvHelperParams(
-
-    goal_gyration_tensor_eigvals_ratio::Float64
-
-    n_particles::Int64
-    max_distance::Float64
-
-    vec_to_neighbour_sums::Vector{SVector{2,Float64}}
-    n_neighbours::Vector{Int64}
-
-    function Params(
         env::Env,
         agent::Agent,
         hook::H,
         n_steps_before_actions_update::Int64,
         goal_gyration_tensor_eigvals_ratio::Float64,
         n_particles::Int64,
-        max_distance::Float64,
     ) where {H<:AbstractHook}
-        n_states = env.n_states
-
         return new{H}(
             env,
             agent,
             hook,
-            fill(0, n_particles),
-            fill(n_states, n_particles),
-            fill(SVector(0.0, 0.0), n_particles),
-            fill(0, n_particles),
             n_steps_before_actions_update,
             goal_gyration_tensor_eigvals_ratio,
             n_particles,
-            max_distance,
+            fill(0, n_particles),
+            fill(0, n_particles),
             fill(SVector(0.0, 0.0), n_particles),
             fill(0, n_particles),
         )
     end
 end
 
-function pre_integration_hook(rl_params::Params)
+abstract type EnvHelper end
-    @simd for id in 1:(rl_params.n_particles)
-        rl_params.vec_to_neighbour_sums[id] = SVector(0.0, 0.0)
-        rl_params.n_neighbours[id] = 0
-    end
 
-    return nothing
+function gen_env_helper(::Env, env_helper_params::EnvHelperParams)
+    return method_not_implemented()
+end
+
+function pre_integration_hook(::EnvHelper)
+    return method_not_implemented()
 end
 
 function state_update_helper_hook(
-    rl_params::Params, id1::Int64, id2::Int64, r⃗₁₂::SVector{2,Float64}
+    ::EnvHelper, id1::Int64, id2::Int64, r⃗₁₂::SVector{2,Float64}
 )
-    rl_params.vec_to_neighbour_sums[id1] += r⃗₁₂
+    return method_not_implemented()
-    rl_params.vec_to_neighbour_sums[id2] -= r⃗₁₂
-
-    rl_params.n_neighbours[id1] += 1
-    rl_params.n_neighbours[id2] += 1
-
-    return nothing
 end
 
-function find_state_ind(state::S, state_space::Vector{S}) where {S<:SVector{2,Interval}}
+function find_state_ind(state::S, state_space::Vector{S}) where {S<:SVector}
     return findfirst(x -> x == state, state_space)
 end
 
@@ -240,89 +212,40 @@ function find_state_interval(value::Float64, state_space::Vector{Interval})::Int
     end
 end
 
-function state_update_hook(rl_params::Params, particles::Vector{Particle})
+function state_update_hook(::EnvHelper, particles::Vector{Particle})
-    @turbo for id in 1:(rl_params.n_particles)
+    return method_not_implemented()
-        rl_params.old_states_ind[id] = rl_params.states_ind[id]
-    end
-
-    env = rl_params.env
-
-    for id in 1:(rl_params.n_particles)
-        n_neighbours = rl_params.n_neighbours[id]
-
-        if n_neighbours == 0
-            state_ind = env.n_states
-        else
-            vec_to_local_center_of_mass = rl_params.vec_to_neighbour_sums[id] / n_neighbours
-
-            distance = sqrt(
-                vec_to_local_center_of_mass[1]^2 + vec_to_local_center_of_mass[2]^2
-            )
-
-            distance_state = find_state_interval(distance, env.distance_state_space)
-
-            si, co = sincos(particles[id].φ)
-
-            direction_angle = angle2(SVector(co, si), vec_to_local_center_of_mass)
-
-            direction_angle_state = find_state_interval(
-                direction_angle, env.direction_angle_state_space
-            )
-
-            state = SVector{2,Interval}(distance_state, direction_angle_state)
-            state_ind = find_state_ind(state, env.state_space)
-        end
-
-        rl_params.states_ind[id] = state_ind
-    end
-
-    return nothing
 end
 
-function get_env_agent_hook(rl_params::Params)
+function get_env_agent_hook(env_helper::EnvHelper)
-    return (rl_params.env, rl_params.agent, rl_params.hook)
+    return (env_helper.params.env, env_helper.params.agent, env_helper.params.hook)
 end
 
-function update_reward!(env::Env, rl_params::Params, particle::Particle)
+function update_reward!(::Env, ::EnvHelper, particle::Particle)
-    id = particle.id
+    return method_not_implemented()
-
-    normalization = (rl_params.max_distance * rl_params.n_particles)
-
-    n_neighbours = rl_params.n_neighbours[id]
-    if n_neighbours == 0
-        env.reward = -(rl_params.max_distance^2) / normalization
-    else
-        vec_to_local_center_of_mass = rl_params.vec_to_neighbour_sums[id] / n_neighbours # TODO: Reuse vec_to_local_center_of_mass from state_update_hook
-        env.reward =
-            -(vec_to_local_center_of_mass[1]^2 + vec_to_local_center_of_mass[2]^2) /
-            normalization
-    end
-
-    return nothing
 end
 
 function update_table_and_actions_hook(
-    rl_params::Params, particle::Particle, first_integration_step::Bool
+    env_helper::EnvHelper, particle::Particle, first_integration_step::Bool
 )
-    env, agent, hook = get_env_agent_hook(rl_params)
+    env, agent, hook = get_env_agent_hook(env_helper)
 
     id = particle.id
 
     if !first_integration_step
         # Old state
-        env.state_ind = rl_params.old_states_ind[id]
+        env.params.state_ind = env_helper.params.old_states_ind[id]
 
-        action_ind = rl_params.actions_ind[id]
+        action_ind = env_helper.params.actions_ind[id]
 
         # Pre act
         agent(PRE_ACT_STAGE, env, action_ind)
         hook(PRE_ACT_STAGE, agent, env, action_ind)
 
         # Update to current state
-        env.state_ind = rl_params.states_ind[id]
+        env.params.state_ind = env_helper.params.states_ind[id]
 
         # Update reward
-        update_reward!(env, rl_params, particle)
+        update_reward!(env, env_helper, particle)
 
         # Post act
         agent(POST_ACT_STAGE, env)
@@ -331,10 +254,10 @@ function update_table_and_actions_hook(
 
     # Update action
     action_ind = agent(env)
-    action = env.action_space[action_ind]
+    action = env.params.action_space[action_ind]
 
-    rl_params.actions[id] = action
+    env_helper.params.actions[id] = action
-    rl_params.actions_ind[id] = action_ind
+    env_helper.params.actions_ind[id] = action_ind
 
     return nothing
 end
@@ -342,10 +265,10 @@ end
 act_hook(::Nothing, args...) = nothing
 
 function act_hook(
-    rl_params::Params, particle::Particle, δt::Float64, si::Float64, co::Float64
+    env_helper::EnvHelper, particle::Particle, δt::Float64, si::Float64, co::Float64
 )
     # Apply action
-    action = rl_params.actions[particle.id]
+    action = env_helper.params.actions[particle.id]
 
     vδt = action[1] * δt
     particle.tmp_c += SVector(vδt * co, vδt * si)
@@ -378,6 +301,8 @@ function gen_agent(n_states::Int64, n_actions::Int64, ϵ_stable::Float64)
 end
 
 function run_rl(;
+    EnvType::Type{E},
+    parent_dir_appendix::String,
     goal_gyration_tensor_eigvals_ratio::Float64,
     n_episodes::Int64=200,
     episode_duration::Float64=50.0,
@@ -385,8 +310,9 @@ function run_rl(;
     n_particles::Int64=100,
     seed::Int64=42,
     ϵ_stable::Float64=0.0001,
-    parent_dir::String="",
+    skin_to_interaction_r_ratio::Float64=DEFAULT_SKIN_TO_INTERACTION_R_RATIO,
-)
+    packing_ratio=0.22,
+) where {E<:Env}
     @assert 0.0 <= goal_gyration_tensor_eigvals_ratio <= 1.0
     @assert n_episodes > 0
     @assert episode_duration > 0
@@ -398,30 +324,33 @@ function run_rl(;
     Random.seed!(seed)
 
     sim_consts = ReCo.gen_sim_consts(
-        n_particles, 0.0; skin_to_interaction_r_ratio=2.0, packing_ratio=0.22
+        n_particles,
+        0.0;
+        skin_to_interaction_r_ratio=skin_to_interaction_r_ratio,
+        packing_ratio=packing_ratio,
     )
-    n_particles = sim_consts.n_particles
+    n_particles = sim_consts.n_particles # This not always equal to the input!
 
-    max_distance = sim_consts.skin_r
+    env = EnvType(sim_consts)
-    env = Env(; max_distance=max_distance)
 
-    agent = gen_agent(env.n_states, env.n_actions, ϵ_stable)
+    agent = gen_agent(env.params.n_states, env.params.n_actions, ϵ_stable)
 
     n_steps_before_actions_update = round(Int64, update_actions_at / sim_consts.δt)
 
     hook = TotalRewardPerEpisode()
 
-    rl_params = Params(
+    env_helper_params = EnvHelperParams(
         env,
         agent,
         hook,
         n_steps_before_actions_update,
         goal_gyration_tensor_eigvals_ratio,
         n_particles,
-        max_distance,
     )
 
-    parent_dir = "RL" * parent_dir
+    env_helper = gen_env_helper(env, env_helper_params)
+
+    parent_dir = "RL_" * parent_dir_appendix
 
     # Pre experiment
     hook(PRE_EXPERIMENT_STAGE, agent, env)
@@ -439,10 +368,13 @@ function run_rl(;
 
         # Episode
         ReCo.run_sim(
-            dir; duration=episode_duration, seed=rand(1:typemax(Int64)), rl_params=rl_params
+            dir;
+            duration=episode_duration,
+            seed=rand(1:typemax(Int64)),
+            env_helper=env_helper,
         )
 
-        env.terminated = true
+        env.params.terminated = true
 
         # Post episode
         hook(POST_EPISODE_STAGE, agent, env)
@@ -456,7 +388,9 @@ function run_rl(;
     # Post experiment
     hook(POST_EXPERIMENT_STAGE, agent, env)
 
-    return rl_params
+    return env_helper
 end
 
+include("LocalCOMEnv.jl")
+
 end # module

src/ReCo.jl

@@ -1,6 +1,6 @@
 module ReCo
 
-export init_sim, run_sim, run_rl, animate
+export init_sim, run_sim, run_rl, animate, LocalCOMEnv
 
 using StaticArrays: SVector
 using OrderedCollections: OrderedDict
@@ -26,7 +26,7 @@ include("setup.jl")
 include("Shape.jl")
 using .Shape
 
-include("RL.jl")
+include("RL/RL.jl")
 using .RL
 
 include("simulation.jl")

src/run.jl

@@ -6,7 +6,7 @@ function run_sim(
     snapshot_at::Float64=0.1,
     seed::Int64=42,
     n_bundle_snapshots::Int64=100,
-    rl_params::Union{RL.Params,Nothing}=nothing,
+    env_helper::Union{RL.EnvHelper,Nothing}=nothing,
 )
     @assert length(dir) > 0
     @assert duration > 0
@@ -111,7 +111,7 @@ function run_sim(
         n_bundles,
         dir,
         save_data,
-        rl_params,
+        env_helper,
     )
 
     return nothing

src/simulation.jl

@@ -35,7 +35,7 @@ end
 function euler!(
     args,
     first_integration_step::Bool,
-    rl_params::Union{RL.Params,Nothing},
+    env_helper::Union{RL.EnvHelper,Nothing},
     state_update_helper_hook::Function,
     state_update_hook::Function,
     update_table_and_actions_hook::Function,
@@ -52,7 +52,7 @@ function euler!(
                 p1_c, p2.c, args.interaction_r², args.half_box_len
             )
 
-            state_update_helper_hook(rl_params, id1, id2, r⃗₁₂)
+            state_update_helper_hook(env_helper, id1, id2, r⃗₁₂)
 
             if overlapping
                 factor = args.c₁ / (distance²^4) * (args.c₂ / (distance²^3) - 1.0)
@@ -64,7 +64,7 @@ function euler!(
         end
     end
 
-    state_update_hook(rl_params, args.particles)
+    state_update_hook(env_helper, args.particles)
 
     @simd for p in args.particles
         si, co = sincos(p.φ)
@@ -75,9 +75,9 @@ function euler!(
 
         restrict_coordinates!(p, args.half_box_len)
 
-        update_table_and_actions_hook(rl_params, p, first_integration_step)
+        update_table_and_actions_hook(env_helper, p, first_integration_step)
 
-        RL.act_hook(rl_params, p, args.δt, si, co)
+        RL.act_hook(env_helper, p, args.δt, si, co)
 
         p.φ += args.c₄ * rand_normal01()
 
@@ -91,8 +91,8 @@ Base.wait(::Nothing) = nothing
 
 gen_run_additional_hooks(::Nothing, args...) = false
 
-function gen_run_additional_hooks(rl_params::RL.Params, integration_step::Int64)
+function gen_run_additional_hooks(env_helper::RL.EnvHelper, integration_step::Int64)
-    return (integration_step % rl_params.n_steps_before_actions_update == 0) ||
+    return (integration_step % env_helper.params.n_steps_before_actions_update == 0) ||
            (integration_step == 1)
 end
 
@@ -105,7 +105,7 @@ function simulate(
     n_bundles::Int64,
     dir::String,
     save_data::Bool,
-    rl_params::Union{RL.Params,Nothing},
+    env_helper::Union{RL.EnvHelper,Nothing},
 )
     bundle_snapshot_counter = 0
 
@@ -143,10 +143,10 @@ function simulate(
             cl = update_verlet_lists!(args, cl)
         end
 
-        run_additional_hooks = gen_run_additional_hooks(rl_params, integration_step)
+        run_additional_hooks = gen_run_additional_hooks(env_helper, integration_step)
 
         if run_additional_hooks
-            RL.pre_integration_hook(rl_params)
+            RL.pre_integration_hook(env_helper)
 
             state_update_helper_hook = RL.state_update_helper_hook
             state_update_hook = RL.state_update_hook
@@ -156,7 +156,7 @@ function simulate(
         euler!(
             args,
             first_integration_step,
-            rl_params,
+            env_helper,
             state_update_helper_hook,
             state_update_hook,
             update_table_and_actions_hook,