diff --git a/src/ReinforcementLearningZoo/Project.toml b/src/ReinforcementLearningZoo/Project.toml
index a9e21fa2f..771c56f77 100644
--- a/src/ReinforcementLearningZoo/Project.toml
+++ b/src/ReinforcementLearningZoo/Project.toml
@@ -19,7 +19,6 @@ Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReinforcementLearningBase = "e575027e-6cd6-5018-9292-cdc6200d2b44"
 ReinforcementLearningCore = "de1b191a-4ae0-4afa-a27b-92d07f46b2d6"
-ReinforcementLearningEnvironments = "25e41dd2-4622-11e9-1641-f1adca772921"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
@@ -40,7 +39,6 @@ IntervalSets = "0.5"
 MacroTools = "0.5"
 ReinforcementLearningBase = "0.9"
 ReinforcementLearningCore = "0.6.1"
-ReinforcementLearningEnvironments = "0.4"
 Requires = "1"
 Setfield = "0.6, 0.7"
 StableRNGs = "1.0"
diff --git a/src/ReinforcementLearningZoo/src/ReinforcementLearningZoo.jl b/src/ReinforcementLearningZoo/src/ReinforcementLearningZoo.jl
index 78695dd72..9f8f4aa23 100644
--- a/src/ReinforcementLearningZoo/src/ReinforcementLearningZoo.jl
+++ b/src/ReinforcementLearningZoo/src/ReinforcementLearningZoo.jl
@@ -6,7 +6,6 @@ export RLZoo
 using CircularArrayBuffers
 using ReinforcementLearningBase
 using ReinforcementLearningCore
-using ReinforcementLearningEnvironments
 using Setfield: @set
 using StableRNGs
 using Logging
diff --git a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2C.jl b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2C.jl
index aacc0f278..6c90b7c3d 100644
--- a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2C.jl
+++ b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2C.jl
@@ -110,3 +110,5 @@ function _update!(learner::A2CLearner, t::CircularArraySARTTrajectory)
     end
     update!(AC, gs)
 end
+
+RLCore.check(::QBasedPolicy{<:A2CLearner}, ::MultiThreadEnv) = nothing
\ No newline at end of file
diff --git a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2CGAE.jl b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2CGAE.jl
index 5271cb02c..c389574b8 100644
--- a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2CGAE.jl
+++ b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/A2CGAE.jl
@@ -113,3 +113,5 @@ function _update!(learner::A2CGAELearner, t::CircularArraySARTTrajectory)
 
     update!(AC, gs)
 end
+
+RLCore.check(::QBasedPolicy{<:A2CGAELearner}, ::MultiThreadEnv) = nothing
\ No newline at end of file
diff --git a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/MAC.jl b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/MAC.jl
index a33b8d946..dc1063c44 100644
--- a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/MAC.jl
+++ b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/MAC.jl
@@ -133,3 +133,5 @@ function _update!(learner::MACLearner, t::CircularArraySARTTrajectory)
     end
     update!(AC.critic, gs2)
 end
+
+RLCore.check(::QBasedPolicy{<:MACLearner}, ::MultiThreadEnv) = nothing
\ No newline at end of file
diff --git a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/multi_thread_env.jl b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/multi_thread_env.jl
new file mode 100644
index 000000000..f8fcc8a4e
--- /dev/null
+++ b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/multi_thread_env.jl
@@ -0,0 +1,148 @@
+export MultiThreadEnv
+
+using Base.Threads:@spawn
+
+"""
+    MultiThreadEnv(envs::Vector{<:AbstractEnv})
+
+Wrap multiple instances of the same environment type into one environment.
+Each environment will run in parallel by leveraging `Threads.@spawn`.
+So remember to set the environment variable `JULIA_NUM_THREADS`!
+"""
+struct MultiThreadEnv{E,S,R,AS,SS,L} <: AbstractEnv
+    envs::Vector{E}
+    states::S
+    rewards::R
+    terminals::BitArray{1}
+    action_space::AS
+    state_space::SS
+    legal_action_space_mask::L
+end
+
+function Base.show(io::IO, t::MIME"text/markdown", env::MultiThreadEnv)
+    s = """
+    # MultiThreadEnv($(length(env)) x $(nameof(env[1])))
+    """
+    show(io, t, Markdown.parse(s))
+end
+
+"""
+    MultiThreadEnv(f, n::Int)
+
+`f` is a lambda function which creates an `AbstractEnv` by calling `f()`.
+"""
+MultiThreadEnv(f, n::Int) = MultiThreadEnv([f() for _ in 1:n])
+
+function MultiThreadEnv(envs::Vector{<:AbstractEnv})
+    n = length(envs)
+    S = state_space(envs[1])
+    s = state(envs[1])
+    if S isa Space
+        S_batch = similar(S, size(S)..., n)
+        s_batch = similar(s, size(s)..., n)
+        for j in 1:n
+            Sₙ = state_space(envs[j])
+            sₙ = state(envs[j])
+            for i in CartesianIndices(size(S))
+                S_batch[i, j] = Sₙ[i]
+                s_batch[i, j] = sₙ[i]
+            end
+        end
+    else
+        S_batch = Space(state_space.(envs))
+        s_batch = state.(envs)
+    end
+
+    A = action_space(envs[1])
+    if A isa Space
+        A_batch = similar(A, size(A)..., n)
+        for j in 1:n
+            Aⱼ = action_space(envs[j])
+            for i in CartesianIndices(size(A))
+                A_batch[i, j] = Aⱼ[i]
+            end
+        end
+    else
+        A_batch = Space(action_space.(envs))
+    end
+
+    r_batch = reward.(envs)
+    t_batch = is_terminated.(envs)
+    if ActionStyle(envs[1]) === FULL_ACTION_SET
+        m_batch = BitArray(undef, size(A_batch))
+        for j in 1:n
+            L = legal_action_space_mask(envs[j])
+            for i in CartesianIndices(size(A))
+                m_batch[i, j] = L[i]
+            end
+        end
+    else
+        m_batch = nothing
+    end
+    MultiThreadEnv(envs, s_batch, r_batch, t_batch, A_batch, S_batch, m_batch)
+end
+
+MacroTools.@forward MultiThreadEnv.envs Base.getindex, Base.length, Base.iterate
+
+function (env::MultiThreadEnv)(actions)
+    @sync for i in 1:length(env)
+        @spawn begin
+            env[i](actions[i])
+        end
+    end
+end
+
+function RLBase.reset!(env::MultiThreadEnv; is_force = false)
+    if is_force
+        for i in 1:length(env)
+            reset!(env[i])
+        end
+    else
+        @sync for i in 1:length(env)
+            if is_terminated(env[i])
+                @spawn begin
+                    reset!(env[i])
+                end
+            end
+        end
+    end
+end
+
+const MULTI_THREAD_ENV_CACHE = IdDict{AbstractEnv,Dict{Symbol,Array}}()
+
+function RLBase.state(env::MultiThreadEnv)
+    N = ndims(env.states)
+    @sync for i in 1:length(env)
+        @spawn selectdim(env.states, N, i) .= state(env[i])
+    end
+    env.states
+end
+
+function RLBase.reward(env::MultiThreadEnv)
+    env.rewards .= reward.(env.envs)
+    env.rewards
+end
+
+function RLBase.is_terminated(env::MultiThreadEnv)
+    env.terminals .= is_terminated.(env.envs)
+    env.terminals
+end
+
+function RLBase.legal_action_space_mask(env::MultiThreadEnv)
+    @sync for i in 1:length(env)
+        @spawn selectdim(env.legal_action_space_mask, N, i) .=
+            legal_action_space_mask(env[i])
+    end
+    env.legal_action_space_mask
+end
+
+RLBase.action_space(env::MultiThreadEnv) = env.action_space
+RLBase.state_space(env::MultiThreadEnv) = env.state_space
+RLBase.legal_action_space(env::MultiThreadEnv) = Space(legal_action_space.(env.envs))
+# RLBase.current_player(env::MultiThreadEnv) = current_player.(env.envs)
+
+for f in RLBase.ENV_API
+    if endswith(String(f), "Style")
+        @eval RLBase.$f(x::MultiThreadEnv) = $f(x[1])
+    end
+end
diff --git a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/run.jl b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/run.jl
index 66c29f13e..7d9af366b 100644
--- a/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/run.jl
+++ b/src/ReinforcementLearningZoo/src/algorithms/policy_gradient/run.jl
@@ -1,3 +1,5 @@
+include("multi_thread_env.jl")
+
 """
 Many policy gradient based algorithms require that the `env` is a
 `MultiThreadEnv` to increase the diversity during training. So the training