From 63285ab023efdc67d69f034c4832c01489d74d10 Mon Sep 17 00:00:00 2001
From: Guillaume Mollard <guillaume.mollard2@gmail.com>
Date: Sat, 29 Jun 2019 14:27:52 +0200
Subject: [PATCH] small fixxes
---
.../experiment_configs/config_example/config.gin | 6 +++---
RLLib_training/render_training_result.py | 8 ++------
RLLib_training/train_experiment.py | 7 ++++++-
3 files changed, 11 insertions(+), 10 deletions(-)
diff --git a/RLLib_training/experiment_configs/config_example/config.gin b/RLLib_training/experiment_configs/config_example/config.gin
index 7a4c7c1..22f8078 100644
--- a/RLLib_training/experiment_configs/config_example/config.gin
+++ b/RLLib_training/experiment_configs/config_example/config.gin
@@ -5,10 +5,10 @@ run_experiment.hidden_sizes = [32, 32]
run_experiment.map_width = 20
run_experiment.map_height = 20
-run_experiment.n_agents = 7#{"grid_search": [3, 4, 5, 6, 7, 8]}
-run_experiment.rail_generator = "complex_rail_generator"
+run_experiment.n_agents = 7 #{"grid_search": [3, 4, 5, 6, 7, 8]}
+run_experiment.rail_generator = "complex_rail_generator" # Change this to "load_env" in order to load a predefined complex scene
run_experiment.nr_extra = 5
-run_experiment.policy_folder_name = "ppo_policy_two_obs_with_predictions_n_agents_{config[n_agents]}_"#_map_size_{config[map_width]}"
+run_experiment.policy_folder_name = "ppo_policy_two_obs_with_predictions_n_agents_{config[n_agents]}_"
run_experiment.seed = 123
diff --git a/RLLib_training/render_training_result.py b/RLLib_training/render_training_result.py
index 021b9c4..90b38a3 100644
--- a/RLLib_training/render_training_result.py
+++ b/RLLib_training/render_training_result.py
@@ -33,8 +33,6 @@ __file_dirname__ = os.path.dirname(os.path.realpath(__file__))
CHECKPOINT_PATH = os.path.join(__file_dirname__, 'experiment_configs', 'config_example', 'ppo_policy_two_obs_with_predictions_n_agents_4_map_size_20q58l5_f7',
'checkpoint_101', 'checkpoint-101')
-CHECKPOINT_PATH = '/home/guillaume/Desktop/distMAgent/ppo_policy_two_obs_with_predictions_n_agents_7_8e5zko1_/checkpoint_1301/checkpoint-1301'
-
N_EPISODES = 10
N_STEPS_PER_EPISODE = 50
@@ -67,8 +65,7 @@ def render_training_result(config):
# Dict with the different policies to train
policy_graphs = {
- config['policy_folder_name'].format(**locals()): (PolicyGraph, obs_space, act_space, {})
- # "ppo_policy": (PolicyGraph, obs_space, act_space, {})
+ "ppo_policy": (PolicyGraph, obs_space, act_space, {})
}
def policy_mapping_fn(agent_id):
@@ -106,8 +103,7 @@ def render_training_result(config):
trainer.restore(CHECKPOINT_PATH)
- # policy = trainer.get_policy("ppo_policy")
- policy = trainer.get_policy(config['policy_folder_name'].format(**locals()))
+ policy = trainer.get_policy("ppo_policy")
preprocessor = preprocessor(obs_space)
env_renderer = RenderTool(env, gl="PIL")
diff --git a/RLLib_training/train_experiment.py b/RLLib_training/train_experiment.py
index 1bac614..e7e9709 100644
--- a/RLLib_training/train_experiment.py
+++ b/RLLib_training/train_experiment.py
@@ -47,12 +47,17 @@ def on_episode_start(info):
def on_episode_end(info):
episode = info['episode']
+
+ # Calculation of a custom score metric: cum of all accumulated rewards, divided by the number of agents
+ # and the number of the maximum time steps of the episode.
score = 0
for k, v in episode._agent_reward_history.items():
score += np.sum(v)
score /= (len(episode._agent_reward_history) * episode.horizon)
+
+ # Calculation of the proportion of solved episodes before the maximum time step
done = 1
- if len(episode._agent_reward_history) == episode.horizon:
+ if len(episode._agent_reward_history[0]) == episode.horizon:
done = 0
episode.custom_metrics["score"] = score
episode.custom_metrics["proportion_episode_solved"] = done
--
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