From cf80f503ab12eec9603e4f2b46fd240b694877eb Mon Sep 17 00:00:00 2001
From: "Egli Adrian (IT-SCI-API-PFI)" <adrian.egli@sbb.ch>
Date: Wed, 16 Dec 2020 15:05:54 +0100
Subject: [PATCH] refactored and added new agent
---
reinforcement_learning/multi_agent_training.py | 10 +++++-----
run.py | 2 +-
utils/agent_action_config.py | 9 ++++-----
3 files changed, 10 insertions(+), 11 deletions(-)
diff --git a/reinforcement_learning/multi_agent_training.py b/reinforcement_learning/multi_agent_training.py
index e2ea4bf..5cb6ba1 100755
--- a/reinforcement_learning/multi_agent_training.py
+++ b/reinforcement_learning/multi_agent_training.py
@@ -267,8 +267,8 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
if info['action_required'][agent_handle]:
update_values[agent_handle] = True
action = policy.act(agent_handle, agent_obs[agent_handle], eps=eps_start)
- action_count[map_action(action, get_action_size())] += 1
- actions_taken.append(map_action(action, get_action_size()))
+ action_count[map_action(action)] += 1
+ actions_taken.append(map_action(action))
else:
# An action is not required if the train hasn't joined the railway network,
# if it already reached its target, or if is currently malfunctioning.
@@ -280,7 +280,7 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
# Environment step
step_timer.start()
- next_obs, all_rewards, done, info = train_env.step(map_actions(action_dict, get_action_size()))
+ next_obs, all_rewards, done, info = train_env.step(map_actions(action_dict))
# Reward shaping .Dead-lock .NotMoving .NotStarted
if False:
@@ -288,7 +288,7 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
for agent_handle in train_env.get_agent_handles():
agent = train_env.agents[agent_handle]
act = action_dict.get(agent_handle, RailEnvActions.DO_NOTHING)
- act = map_action(act, get_action_size())
+ act = map_action(act)
if agent.status == RailAgentStatus.ACTIVE:
all_rewards[agent_handle] = 0.0
if done[agent_handle] == False:
@@ -494,7 +494,7 @@ def eval_policy(env, tree_observation, policy, train_params, obs_params):
action = policy.act(agent, agent_obs[agent], eps=0.0)
action_dict.update({agent: action})
policy.end_step(train=False)
- obs, all_rewards, done, info = env.step(map_actions(action_dict, get_action_size()))
+ obs, all_rewards, done, info = env.step(map_actions(action_dict))
for agent in env.get_agent_handles():
score += all_rewards[agent]
diff --git a/run.py b/run.py
index 998add0..8e0535b 100644
--- a/run.py
+++ b/run.py
@@ -214,7 +214,7 @@ while True:
time_taken_by_controller.append(agent_time)
time_start = time.time()
- _, all_rewards, done, info = remote_client.env_step(map_actions(action_dict, get_action_size))
+ _, all_rewards, done, info = remote_client.env_step(map_actions(action_dict))
step_time = time.time() - time_start
time_taken_per_step.append(step_time)
diff --git a/utils/agent_action_config.py b/utils/agent_action_config.py
index dceba55..3a84875 100644
--- a/utils/agent_action_config.py
+++ b/utils/agent_action_config.py
@@ -1,4 +1,3 @@
-
def get_flatland_full_action_size():
# The action space of flatland is 5 discrete actions
return 5
@@ -9,9 +8,9 @@ def get_action_size():
return 4
-def map_actions(actions, action_size):
+def map_actions(actions):
# Map the
- if action_size == get_flatland_full_action_size():
+ if get_action_size() == get_flatland_full_action_size():
return actions
for key in actions:
value = actions.get(key, 0)
@@ -19,7 +18,7 @@ def map_actions(actions, action_size):
return actions
-def map_action(action, action_size):
- if action_size == get_flatland_full_action_size():
+def map_action(action):
+ if get_action_size() == get_flatland_full_action_size():
return action
return action + 1
--
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