From 287f810998de667e11bc68ae501c2f048eb8b972 Mon Sep 17 00:00:00 2001
From: MLErik <baerenjesus@gmail.com>
Date: Sun, 11 Aug 2019 10:04:12 -0400
Subject: [PATCH] testing that agents don't move in multi-speed setup
---
tests/test_multi_speed.py | 35 ++++++++++++++++-------------------
1 file changed, 16 insertions(+), 19 deletions(-)
diff --git a/tests/test_multi_speed.py b/tests/test_multi_speed.py
index 46310a2c..8b546871 100644
--- a/tests/test_multi_speed.py
+++ b/tests/test_multi_speed.py
@@ -9,11 +9,6 @@ np.random.seed(1)
# Training on simple small tasks is the best way to get familiar with the environment
#
-env = RailEnv(width=50,
- height=50,
- rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=8, max_dist=99999, seed=0),
- number_of_agents=5)
-
class RandomAgent:
@@ -46,18 +41,19 @@ class RandomAgent:
return
-# Initialize the agent with the parameters corresponding to the environment and observation_builder
-agent = RandomAgent(218, 4)
-n_trials = 5
-
-
-# Empty dictionary for all agent action
-action_dict = dict()
-
-
-# Set all the different speeds
-
def test_multi_speed_init():
+ env = RailEnv(width=50,
+ height=50,
+ rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=8, max_dist=99999,
+ seed=0),
+ number_of_agents=5)
+ # Initialize the agent with the parameters corresponding to the environment and observation_builder
+ agent = RandomAgent(218, 4)
+
+ # Empty dictionary for all agent action
+ action_dict = dict()
+
+ # Set all the different speeds
# Reset environment and get initial observations for all agents
env.reset()
# Here you can also further enhance the provided observation by means of normalization
@@ -66,7 +62,7 @@ def test_multi_speed_init():
for i_agent in range(env.get_num_agents()):
env.agents[i_agent].speed_data['speed'] = 1. / (i_agent + 1)
old_pos.append(env.agents[i_agent].position)
- score = 0
+
# Run episode
for step in range(100):
@@ -74,14 +70,15 @@ def test_multi_speed_init():
for a in range(env.get_num_agents()):
action = agent.act(0)
action_dict.update({a: action})
- # Check that agent did not move inbetween its speed updates
+
+ # Check that agent did not move in between its speed updates
assert old_pos[a] == env.agents[a].position
# Environment step which returns the observations for all agents, their corresponding
# reward and whether their are done
_, _, _, _ = env.step(action_dict)
- # Update old position
+ # Update old position whenever an agent was allowed to move
for i_agent in range(env.get_num_agents()):
if (step + 1) % (i_agent + 1) == 0:
print(step, i_agent, env.agents[a].position)
--
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