From 0ecec4750954ec9a29968f6d586036b7f8db19a2 Mon Sep 17 00:00:00 2001
From: MLErik <baerenjesus@gmail.com>
Date: Sun, 11 Aug 2019 08:58:59 -0400
Subject: [PATCH] initial commit for speed tests and multi speed
initialization. waiting for other merges first
---
tests/test_multi_speed.py | 30 ++++++++++++++++++------------
1 file changed, 18 insertions(+), 12 deletions(-)
diff --git a/tests/test_multi_speed.py b/tests/test_multi_speed.py
index b410754c..46310a2c 100644
--- a/tests/test_multi_speed.py
+++ b/tests/test_multi_speed.py
@@ -26,7 +26,7 @@ class RandomAgent:
:param state: input is the observation of the agent
:return: returns an action
"""
- return np.random.choice(np.arange(self.action_size))
+ return np.random.choice([1, 2, 3])
def step(self, memories):
"""
@@ -50,34 +50,40 @@ class RandomAgent:
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():
# Reset environment and get initial observations for all agents
- obs = env.reset()
+ env.reset()
# Here you can also further enhance the provided observation by means of normalization
# See training navigation example in the baseline repository
+ old_pos = []
for i_agent in range(env.get_num_agents()):
- env.agents[i_agent].speed_data['speed'] = 1. / np.random.randint(1, 10)
+ 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):
+
# Chose an action for each agent in the environment
for a in range(env.get_num_agents()):
- action = agent.act(obs[a])
+ action = agent.act(0)
action_dict.update({a: action})
+ # Check that agent did not move inbetween 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
- next_obs, all_rewards, done, _ = env.step(action_dict)
+ _, _, _, _ = env.step(action_dict)
- # 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]
+ # Update old position
+ for i_agent in range(env.get_num_agents()):
+ if (step + 1) % (i_agent + 1) == 0:
+ print(step, i_agent, env.agents[a].position)
- obs = next_obs.copy()
- if done['__all__']:
- break
+ old_pos[i_agent] = env.agents[i_agent].position
--
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