Corrected for numba deprecation

Enable the ability to render out scenes to play back data

rltorch/agents/QEPAgent.py

@@ -34,6 +34,7 @@ class QEPAgent:
         self.value_net.model.to(self.value_net.device)
         self.policy_net.model.state_dict(checkpoint['policy'])
         self.policy_net.model.to(self.policy_net.device)
+        if self.target_value_net is not None:
             self.target_net.sync()
 
     def fitness(self, policy_net, value_net, state_batch):

rltorch/env/simulate.py

@@ -1,7 +1,8 @@
 from copy import deepcopy 
 import rltorch
+import time
 
-def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger = None, name = ""):
+def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger = None, name = "", render = False):
   for episode in range(total_episodes):
     state = env.reset()
     done = False
@@ -9,6 +10,9 @@ def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger
     while not done:
       action = actor.act(state)
       next_state, reward, done, _ = env.step(action)
+      if render:
+        env.render()
+        time.sleep(0.01)
 
       episode_reward = episode_reward + reward
       if memory is not None:
@@ -24,7 +28,7 @@ def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger
 
 
 class EnvironmentRunSync():
-  def __init__(self, env, actor, config, memory = None, logwriter = None, name = ""):
+  def __init__(self, env, actor, config, memory = None, logwriter = None, name = "", render = False):
     self.env = env
     self.name = name
     self.actor = actor
@@ -34,6 +38,7 @@ class EnvironmentRunSync():
     self.episode_num = 1
     self.episode_reward = 0
     self.last_state = env.reset()
+    self.render = render
 
   def run(self, iterations):
     state = self.last_state
@@ -41,6 +46,8 @@ class EnvironmentRunSync():
     for _ in range(iterations):
       action = self.actor.act(state)
       next_state, reward, done, _ = self.env.step(action)
+      if self.render:
+        self.env.render()
        
       self.episode_reward += reward
       if self.memory is not None:

rltorch/memory/PrioritizedReplayMemory.py

@@ -34,7 +34,7 @@ class SegmentTree(object):
         self._value = [neutral_element for _ in range(2 * capacity)]
         self._operation = operation
 
-    @jit
+    @jit(forceobj = True)
     def _reduce_helper(self, start, end, node, node_start, node_end):
         if start == node_start and end == node_end:
             return self._value[node]
@@ -50,7 +50,7 @@ class SegmentTree(object):
                     self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end)
                 )
 
-    @jit
+    @jit(forceobj = True)
     def reduce(self, start=0, end=None):
         """Returns result of applying `self.operation`
         to a contiguous subsequence of the array.
@@ -73,7 +73,7 @@ class SegmentTree(object):
         end -= 1
         return self._reduce_helper(start, end, 1, 0, self._capacity - 1)
 
-    @jit
+    @jit(forceobj = True)
     def __setitem__(self, idx, val):
         # index of the leaf
         idx += self._capacity
@@ -86,7 +86,7 @@ class SegmentTree(object):
             )
             idx //= 2
 
-    @jit
+    @jit(forceobj = True)
     def __getitem__(self, idx):
         assert 0 <= idx < self._capacity
         return self._value[self._capacity + idx]
@@ -100,12 +100,12 @@ class SumSegmentTree(SegmentTree):
             neutral_element=0.0
         )
 
-    @jit
+    @jit(forceobj = True)
     def sum(self, start=0, end=None):
         """Returns arr[start] + ... + arr[end]"""
         return super(SumSegmentTree, self).reduce(start, end)
 
-    @jit
+    @jit(forceobj = True)
     def find_prefixsum_idx(self, prefixsum):
         """Find the highest index `i` in the array such that
             sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
@@ -140,7 +140,7 @@ class MinSegmentTree(SegmentTree):
             neutral_element=float('inf')
         )
 
-    @jit
+    @jit(forceobj = True)
     def min(self, start=0, end=None):
         """Returns min(arr[start], ...,  arr[end])"""
         return super(MinSegmentTree, self).reduce(start, end)
@@ -179,7 +179,7 @@ class PrioritizedReplayMemory(ReplayMemory):
         self._it_sum[idx] = self._max_priority ** self._alpha
         self._it_min[idx] = self._max_priority ** self._alpha
 
-    @jit
+    @jit(forceobj = True)
     def _sample_proportional(self, batch_size):
         res = []
         p_total = self._it_sum.sum(0, len(self.memory) - 1)
@@ -239,7 +239,7 @@ class PrioritizedReplayMemory(ReplayMemory):
         batch = list(zip(*encoded_sample, weights, idxes))
         return batch
 
-    @jit
+    @jit(forceobj = True)
     def update_priorities(self, idxes, priorities):
         """Update priorities of sampled transitions.
         sets priority of transition at index idxes[i] in buffer