From 3af1729d0c13087af68cedd9cccf3371817f9ad6 Mon Sep 17 00:00:00 2001
From: hagrid67 <jdhwatson@gmail.com>
Date: Mon, 20 May 2019 17:27:50 +0100
Subject: [PATCH] fixed lint, commented out some untouched Agent / pytorch refs
---
examples/play_model.py | 34 +++++++++++++++++++---------------
1 file changed, 19 insertions(+), 15 deletions(-)
diff --git a/examples/play_model.py b/examples/play_model.py
index 777f2d34..bbbcfffd 100644
--- a/examples/play_model.py
+++ b/examples/play_model.py
@@ -26,9 +26,11 @@ class Player(object):
self.scores = []
self.dones_list = []
self.action_prob = [0]*4
+
+ # Removing refs to a real agent for now.
# 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(
+ # self.agent.qnetwork_local.load_state_dict(torch.load(
# '../flatland/flatland/baselines/Nets/avoid_checkpoint15000.pth'))
self.iFrame = 0
@@ -56,8 +58,11 @@ class Player(object):
# Pass the (stored) observation to the agent network and retrieve the action
for handle in env.get_agent_handles():
+ # Real Agent
# action = self.agent.act(np.array(self.obs[handle]), eps=self.eps)
+ # Random actions
action = random.randint(0, 3)
+ # Numpy version uses single random sequence
# action = np.random.randint(0, 4, size=1)
self.action_prob[action] += 1
self.action_dict.update({handle: action})
@@ -65,7 +70,6 @@ class Player(object):
# 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))
@@ -117,7 +121,7 @@ def main(render=True, delay=0.0, n_trials=3, n_steps=50, sGL="QT"):
for trials in range(1, n_trials + 1):
- # Reset environment8
+ # Reset environment
oPlayer.reset()
env_renderer.set_new_rail()
@@ -156,8 +160,6 @@ def main_old(render=True, delay=0.0):
env_renderer = RenderTool(env, gl="QTSVG")
# env_renderer = RenderTool(env, gl="QT")
- state_size = 105
- action_size = 4
n_trials = 9999
eps = 1.
eps_end = 0.005
@@ -168,7 +170,11 @@ def main_old(render=True, delay=0.0):
scores = []
dones_list = []
action_prob = [0]*4
- agent = Agent(state_size, action_size, "FC", 0)
+
+ # Real Agent
+ # state_size = 105
+ # action_size = 4
+ # agent = Agent(state_size, action_size, "FC", 0)
# agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint9900.pth'))
def max_lt(seq, val):
@@ -188,12 +194,10 @@ def main_old(render=True, delay=0.0):
tStart = time.time()
for trials in range(1, n_trials + 1):
- # Reset environment8
+ # Reset environment
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)
@@ -210,13 +214,12 @@ def main_old(render=True, delay=0.0):
# print(step)
# Action
for a in range(env.get_num_agents()):
- action = random.randint(0,3) # 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})
if render:
env_renderer.renderEnv(show=True, frames=True, iEpisode=trials, iStep=step, action_dict=action_dict)
- #time.sleep(10)
if delay > 0:
time.sleep(delay)
@@ -224,15 +227,16 @@ def main_old(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)
+
# Update replay buffer and train agent
- for a in range(env.get_num_agents()):
- agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
- score += all_rewards[a]
+ # only needed for "real" agent
+ # for a in range(env.get_num_agents()):
+ # 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__']:
--
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