testing reinforcement Learning package

reinforecementLearning/Lottery.jl

@@ -0,0 +1 @@
+# creating your own environment 

reinforecementLearning/One-dimensionalRandomWalk.jl

@@ -4,14 +4,75 @@ using ReinforcementLearning
 env = RandomWalk1D()
 
 
-state_space(env)
+S = state_space(env)
 
-action_space(env)
+s = state(env)
 
+A = action_space(env)
+
+is_terminated(env)
+
+while true
+    act!(env, rand(A))
+    is_terminated(env) && break
+end 
 
 state(env)
 
-is_terminated(env)
+# use random policy on eneviornment 
+run(
+    RandomPolicy(), 
+    RandomWalk1D(), 
+    StopAfterNEpisodes(10), 
+    TotalRewardPerEpisode()
+)
+
+
+#QBasedLearner 
+
+NS = length(S)
+NA = length(A)
+
+policy = QBasedPolicy(
+    learner = TDLearner(
+        TabularQApproximator(
+            n_state= NS,
+            n_action = NA,
+        ),
+        :SARS
+    ), 
+    explorer = EpsilonGreedyExplorer(0.1)
+)
+
+
+
+run(
+    policy, 
+    RandomWalk1D(), 
+    StopAfterNEpisodes(10), 
+    TotalRewardPerEpisode()
+)
+
+
+# learn the policy 
+
+using ReinforcementLearningTrajectories
+
+trajectory =Trajectory(
+    ElasticArraySARTSATraces(;
+    state = Int64 => (), 
+    action = Int64 => (), 
+    reward = Float64 => (), 
+    terminal = Bool => (),
+    ),
+    DummySampler(), 
+    InsertSampleRatioController(),
+)
+
+agent = Agent(
+    policy = RandomPolicy(),
+    trajectory = trajectory
+)
 
 
-rand(2)
+run(agent, env, StopAfterNEpisodes(10), TotalRewardPerEpisode())