Merge pull request #190 from JuliaReinforcementLearning/auto-juliaformatter-pr

Automatic JuliaFormatter.jl run

Author: norci

src/policies/agents/agent.jl

@@ -24,7 +24,8 @@ functor(x::Agent) = (policy = x.policy,), y -> @set x.policy = y.policy
 (agent::Agent)(env) = agent.policy(env)
 
 function check(agent::Agent, env::AbstractEnv)
-    if ActionStyle(env) === FULL_ACTION_SET && !haskey(agent.trajectory, :legal_actions_mask)
+    if ActionStyle(env) === FULL_ACTION_SET &&
+       !haskey(agent.trajectory, :legal_actions_mask)
         @warn "The env[$(nameof(env))] is of FULL_ACTION_SET, but I can not find a trace named :legal_actions_mask in the trajectory"
     end
     check(agent.policy, env)

src/policies/q_based_policies/q_based_policy.jl

@@ -20,11 +20,11 @@ Flux.functor(x::QBasedPolicy) = (learner = x.learner,), y -> @set x.learner = y.
 
 (π::QBasedPolicy)(env) = π(env, ActionStyle(env))
 (π::QBasedPolicy)(env, ::MinimalActionSet) = π.explorer(π.learner(env))
-(π::QBasedPolicy)(env, ::FullActionSet) = π.explorer(π.learner(env), legal_action_space_mask(env))
+(π::QBasedPolicy)(env, ::FullActionSet) =
+    π.explorer(π.learner(env), legal_action_space_mask(env))
 
 RLBase.prob(p::QBasedPolicy, env) = prob(p, env, ActionStyle(env))
-RLBase.prob(p::QBasedPolicy, env, ::MinimalActionSet) =
-    prob(p.explorer, p.learner(env))
+RLBase.prob(p::QBasedPolicy, env, ::MinimalActionSet) = prob(p.explorer, p.learner(env))
 RLBase.prob(p::QBasedPolicy, env, ::FullActionSet) =
     prob(p.explorer, p.learner(env), legal_action_space_mask(env))
 
@@ -36,7 +36,7 @@ RLBase.update!(p::QBasedPolicy, trajectory::AbstractTrajectory) =
 function check(p::QBasedPolicy, env::AbstractEnv)
     A = action_space(env)
     if (A isa AbstractVector && A == 1:length(A)) ||
-        (A isa Tuple && A == Tuple(1:length(A)))
+       (A isa Tuple && A == Tuple(1:length(A)))
         # this is expected
     else
         @warn "Applying a QBasedPolicy to an environment with a unknown action space. Maybe convert the environment with `discrete2standard_discrete` in ReinforcementLearningEnvironments.jl first or redesign the environment."

src/policies/random_policy.jl

@@ -20,27 +20,35 @@ end
 
 Random.seed!(p::RandomPolicy, seed) = Random.seed!(p.rng, seed)
 
-RandomPolicy(s=nothing; rng = Random.GLOBAL_RNG) = RandomPolicy(s, rng)
+RandomPolicy(s = nothing; rng = Random.GLOBAL_RNG) = RandomPolicy(s, rng)
 
 (p::RandomPolicy{Nothing})(env) = rand(p.rng, legal_action_space(env))
 (p::RandomPolicy)(env) = rand(p.rng, p.action_space)
 
 function RLBase.prob(p::RandomPolicy{<:Union{AbstractVector,Tuple}}, env::AbstractEnv)
     n = length(p.action_space)
-    Categorical(fill(1/n, n); check_args=false)
+    Categorical(fill(1 / n, n); check_args = false)
 end
 
 RLBase.prob(p::RandomPolicy{Nothing}, env::AbstractEnv) = prob(p, env, ChanceStyle(env))
 
-function RLBase.prob(p::RandomPolicy{Nothing}, env::AbstractEnv, ::RLBase.AbstractChanceStyle)
+function RLBase.prob(
+    p::RandomPolicy{Nothing},
+    env::AbstractEnv,
+    ::RLBase.AbstractChanceStyle,
+)
     mask = legal_action_space_mask(env)
     n = sum(mask)
     prob = zeros(length(mask))
     prob[mask] .= 1 / n
     prob
 end
 
-function RLBase.prob(p::RandomPolicy{Nothing}, env::AbstractEnv, ::RLBase.ExplicitStochastic)
+function RLBase.prob(
+    p::RandomPolicy{Nothing},
+    env::AbstractEnv,
+    ::RLBase.ExplicitStochastic,
+)
     if current_player(env) == chance_player(env)
         prob(env, chance_player(env))
     else

src/policies/random_start_policy.jl

@@ -25,4 +25,4 @@ for f in (:prob, :priority)
             $f(p.random_policy, args...)
         end
     end
-end
+end

test/components/agents.jl

@@ -1,2 +1 @@
-@testset "Agent" begin
-end
+@testset "Agent" begin end