diff --git a/examples/play_model.py b/examples/play_model.py
index 62726c24c96be0e5dae2f4840e18da452163b7ac..e502bd28c419cbd8ac473b8c736e7a424000c229 100644
--- a/examples/play_model.py
+++ b/examples/play_model.py
@@ -28,8 +28,8 @@ class Player(object):
self.action_prob = [0]*4
self.agent = Agent(self.state_size, self.action_size, "FC", 0)
# self.agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint9900.pth'))
- self.agent.qnetwork_local.load_state_dict(torch.load(
- '../flatland/flatland/baselines/Nets/avoid_checkpoint15000.pth'))
+ #self.agent.qnetwork_local.load_state_dict(torch.load(
+ # '../flatland/flatland/baselines/Nets/avoid_checkpoint15000.pth'))
self.iFrame = 0
self.tStart = time.time()
@@ -47,20 +47,26 @@ class Player(object):
self.score = 0
self.env_done = 0
+ def reset(self):
+ self.obs = self.env.reset()
+ return self.obs
+
def step(self):
env = self.env
# Pass the (stored) observation to the agent network and retrieve the action
- #for handle in env.get_agent_handles():
for handle in env.get_agent_handles():
- action = self.agent.act(np.array(self.obs[handle]), eps=self.eps)
+ # action = self.agent.act(np.array(self.obs[handle]), eps=self.eps)
+ action = random.randint(0, 3)
+ # action = np.random.randint(0, 4, size=1)
self.action_prob[action] += 1
self.action_dict.update({handle: action})
# Environment step - pass the agent actions to the environment,
# retrieve the response - observations, rewards, dones
next_obs, all_rewards, done, _ = self.env.step(self.action_dict)
-
+ next_obs = next_obs
+
for handle in env.get_agent_handles():
norm = max(1, max_lt(next_obs[handle], np.inf))
next_obs[handle] = np.clip(np.array(next_obs[handle]) / norm, -1, 1)
@@ -93,7 +99,49 @@ def max_lt(seq, val):
return None
-def main(render=True, delay=0.0):
+def main(render=True, delay=0.0, n_trials=3, n_steps=50):
+ random.seed(1)
+ np.random.seed(1)
+
+ # Example generate a random rail
+ env = RailEnv(width=15, height=15,
+ rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=20, min_dist=12),
+ number_of_agents=5)
+
+ if render:
+ # env_renderer = RenderTool(env, gl="QTSVG")
+ env_renderer = RenderTool(env, gl="QT")
+
+ oPlayer = Player(env)
+
+ for trials in range(1, n_trials + 1):
+
+ # Reset environment8
+ oPlayer.reset()
+ env_renderer.set_new_rail()
+
+ # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
+
+ # score = 0
+ # env_done = 0
+
+ # Run episode
+ for step in range(n_steps):
+ oPlayer.step()
+ if render:
+ env_renderer.renderEnv(show=True, frames=True, iEpisode=trials, iStep=step,
+ action_dict=oPlayer.action_dict)
+ # time.sleep(10)
+ if delay > 0:
+ time.sleep(delay)
+
+
+def main_old(render=True, delay=0.0):
+ ''' DEPRECATED main which drives agent directly
+ Please use the new main() which creates a Player object which is also used by the Editor.
+ Please fix any bugs in main() and Player rather than here.
+ Will delete this one shortly.
+ '''
random.seed(1)
np.random.seed(1)
@@ -139,10 +187,12 @@ def main(render=True, delay=0.0):
tStart = time.time()
for trials in range(1, n_trials + 1):
- # Reset environment
+ # Reset environment8
obs = env.reset()
env_renderer.set_new_rail()
+ #obs = obs[0]
+
for a in range(env.get_num_agents()):
norm = max(1, max_lt(obs[a], np.inf))
obs[a] = np.clip(np.array(obs[a]) / norm, -1, 1)
@@ -159,7 +209,7 @@ def main(render=True, delay=0.0):
# print(step)
# Action
for a in range(env.get_num_agents()):
- action = agent.act(np.array(obs[a]), eps=eps)
+ action = random.randint(0,3) # agent.act(np.array(obs[a]), eps=eps)
action_prob[action] += 1
action_dict.update({a: action})
@@ -173,6 +223,8 @@ def main(render=True, delay=0.0):
# Environment step
next_obs, all_rewards, done, _ = env.step(action_dict)
+ #next_obs = next_obs[0]
+
for a in range(env.get_num_agents()):
norm = max(1, max_lt(next_obs[a], np.inf))
next_obs[a] = np.clip(np.array(next_obs[a]) / norm, -1, 1)
@@ -181,7 +233,6 @@ def main(render=True, delay=0.0):
agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
score += all_rewards[a]
-
obs = next_obs.copy()
if done['__all__']:
env_done = 1
diff --git a/flatland/envs/observations.py b/flatland/envs/observations.py
index 0fdec364ecc3d8415b27e2101c339d6f49e89022..651d83520ee1f492ea71ba6ddf82cfa5f9093964 100644
--- a/flatland/envs/observations.py
+++ b/flatland/envs/observations.py
@@ -219,8 +219,8 @@ class TreeObsForRailEnv(ObservationBuilder):
if possible_transitions[branch_direction]:
new_cell = self._new_position(agent.position, branch_direction)
- branch_observation, branch_visited = self._explore_branch(handle, new_cell, branch_direction, root_observation,
- 1)
+ branch_observation, branch_visited = \
+ self._explore_branch(handle, new_cell, branch_direction, root_observation, 1)
observation = observation + branch_observation
visited = visited.union(branch_visited)
else:
diff --git a/flatland/utils/rendertools.py b/flatland/utils/rendertools.py
index 34f3e9fa6857e86f4d99d211784d983a2e2a1e75..e9a94ec1fd18635b69d5f4bc47ce1d4f43daffcd 100644
--- a/flatland/utils/rendertools.py
+++ b/flatland/utils/rendertools.py
@@ -474,8 +474,8 @@ class RenderTool(object):
def renderObs(self, agent_handles, observation_dict):
"""
- Render the extent of the observation of each agent. All cells that appear in the agent obsrevation will be
- highlighted.
+ Render the extent of the observation of each agent. All cells that appear in the agent
+ observation will be highlighted.
:param agent_handles: List of agent indices to adapt color and get correct observation
:param observation_dict: dictionary containing sets of cells of the agent observation
diff --git a/tests/test_rendertools.py b/tests/test_rendertools.py
index 245f2f327524653b3cf03bf921f6db6b0d4b51fb..3259ed387f1a09b7a6a5e73fe7976cc78f02f32f 100644
--- a/tests/test_rendertools.py
+++ b/tests/test_rendertools.py
@@ -6,10 +6,6 @@ Tests for `flatland` package.
from flatland.envs.rail_env import RailEnv, random_rail_generator
import numpy as np
-#<<<<<<< HEAD
-#=======
-# import os
-#>>>>>>> dc2fa1ee0244b15c76d89ab768c5e1bbd2716147
import sys
import matplotlib.pyplot as plt