Format .jl files (#69)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

src/environments/classic_control/cartpole.jl

@@ -143,13 +143,13 @@ function RLBase.render(env::CartPoleEnv)
     clearws()
     setviewport(0, 1, 0, 1)
     xthreshold = env.params.xthreshold
-    setwindow(-xthreshold, xthreshold, -.1, l + .1)
-    fillarea([x-.5, x-.5, x+.5, x+.5], [-.05, 0, 0, -.05])
+    setwindow(-xthreshold, xthreshold, -.1, l + 0.1)
+    fillarea([x - 0.5, x - 0.5, x + 0.5, x + 0.5], [-.05, 0, 0, -.05])
     setlinecolorind(4)
     setlinewidth(3)
     polyline([x, x + l * sin(theta)], [0, l * cos(theta)])
     setlinecolorind(2)
-    drawarrow(x + (a == 1) - .5, -.025, x + 1.4 * (a==1) - .7, -.025)
-    plotendofepisode(xthreshold - .2, l, d)
+    drawarrow(x + (a == 1) - 0.5, -.025, x + 1.4 * (a == 1) - 0.7, -.025)
+    plotendofepisode(xthreshold - 0.2, l, d)
     updatews()
 end

src/environments/gym.jl

@@ -6,7 +6,8 @@ function GymEnv(name::String)
         error("Cannot import module 'gym'.\n\nIf you did not yet install it, try running\n`ReinforcementLearningEnvironments.install_gym()`\n")
     end
     gym = pyimport("gym")
-    pyenv = try gym.make(name)
+    pyenv = try
+        gym.make(name)
     catch e
         error("Gym environment $name not found.\n\nRun `ReinforcementLearningEnvironments.list_gym_env_names()` to find supported environments.\n")
     end
@@ -113,7 +114,7 @@ end
 """
 function install_gym(; packages = ["gym", "pybullet"])
     # Use eventual proxy info
-    proxy_arg=String[]
+    proxy_arg = String[]
     if haskey(ENV, "http_proxy")
         push!(proxy_arg, "--proxy")
         push!(proxy_arg, ENV["http_proxy"])

src/environments/open_spiel.jl

@@ -1,9 +1,26 @@
-import .OpenSpiel: load_game, get_type, provides_information_state_tensor,
-provides_observation_tensor, dynamics, new_initial_state, chance_mode,
-is_chance_node, information_state_tensor, information_state_tensor_size,
-num_distinct_actions, num_players, apply_action, current_player, player_reward,
-legal_actions, legal_actions_mask, rewards, history, observation_tensor_size,
-observation_tensor, chance_outcomes
+import .OpenSpiel:
+    load_game,
+    get_type,
+    provides_information_state_tensor,
+    provides_observation_tensor,
+    dynamics,
+    new_initial_state,
+    chance_mode,
+    is_chance_node,
+    information_state_tensor,
+    information_state_tensor_size,
+    num_distinct_actions,
+    num_players,
+    apply_action,
+    current_player,
+    player_reward,
+    legal_actions,
+    legal_actions_mask,
+    rewards,
+    history,
+    observation_tensor_size,
+    observation_tensor,
+    chance_outcomes
 using StatsBase: sample, weights
 
 

src/environments/structs.jl

@@ -50,4 +50,3 @@ struct OpenSpielObs{O,D,S}
     state::S
     player::Int32
 end
-