diff --git a/RLLib_training/README.md b/RLLib_training/README.md
index 21665a54eea1595b1679ebd587392cb1d5725eea..8bda956f226af1c7ef4c7e1237b447cf7af4327a 100644
--- a/RLLib_training/README.md
+++ b/RLLib_training/README.md
@@ -1,9 +1,10 @@
This repository allows to run Rail Environment multi agent training with the RLLib Library.
## Installation:
+
+To run scripts of this repository, the deep learning library tensorflow should be installed, along with the following packages:
```sh
-pip install ray
-pip install gin-config
+pip install gym ray==0.7.0 gin-config opencv-python lz4 psutil
```
To start a training with different parameters, you can create a folder containing a config.gin file (see example in `experiment_configs/config_example/config.gin`.
@@ -57,7 +58,7 @@ More documentation on how to use gin-config can be found on the github repositor
## Run an example:
To start a training on a 20X20 map, with different numbers of agents initialized at each episode, on can run the train_experiment.py script:
```
-python baselines/RLLib_training/train_experiment.py
+python RLLib_training/train_experiment.py
```
This will load the gin config file in the folder `experiment_configs/config_examples`.
diff --git a/sequential_agent/run_test.py b/sequential_agent/run_test.py
index 970d6aadb8aeec1086afb16c257ad3cd65902f45..d0b9ce70f50465a58885a9b1feb754791bb49f34 100644
--- a/sequential_agent/run_test.py
+++ b/sequential_agent/run_test.py
@@ -8,11 +8,11 @@ import numpy as np
np.random.seed(2)
"""
-file_name = "./railway/complex_scene.pkl"
+file_name = "../torch_training/railway/complex_scene.pkl"
env = RailEnv(width=10,
height=20,
rail_generator=rail_from_file(file_name),
- obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()))
+ obs_builder_object=TreeObsForRailEnv(max_depth=1, predictor=ShortestPathPredictorForRailEnv()))
x_dim = env.width
y_dim = env.height
@@ -38,8 +38,8 @@ observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestP
env_renderer = RenderTool(env, gl="PILSVG", )
handle = env.get_agent_handles()
n_trials = 1
-max_steps = 3 * (env.height + env.width)
-record_images = True
+max_steps = 100 * (env.height + env.width)
+record_images = False
agent = OrderedAgent()
action_dict = dict()
@@ -63,6 +63,7 @@ for trials in range(1, n_trials + 1):
for a in range(env.get_num_agents()):
if done[a]:
acting_agent += 1
+ print(acting_agent)
if a == acting_agent:
action = agent.act(obs[a], eps=0)
else:
diff --git a/sequential_agent/simple_order_agent.py b/sequential_agent/simple_order_agent.py
index 6e888c51ab7210062ee6efb9862cd78e5a61ca5a..3feff350e94226f157559036abdaea8d5dc18bf9 100644
--- a/sequential_agent/simple_order_agent.py
+++ b/sequential_agent/simple_order_agent.py
@@ -18,7 +18,7 @@ class OrderedAgent:
min_dist = min_lt(distance, 0)
min_direction = np.where(distance == min_dist)
if len(min_direction[0]) > 1:
- return min_direction[0][0] + 1
+ return min_direction[0][-1] + 1
return min_direction[0] + 1
def step(self, memories):
diff --git a/torch_training/Nets/avoid_checkpoint15000.pth b/torch_training/Nets/avoid_checkpoint15000.pth
index 9d1936ab4a1d51530662b589423f78c0ccb57c44..bb681d11151a13c54c78c22ac7dd421eea45ed32 100644
Binary files a/torch_training/Nets/avoid_checkpoint15000.pth and b/torch_training/Nets/avoid_checkpoint15000.pth differ
diff --git a/torch_training/Nets/avoid_checkpoint30000.pth b/torch_training/Nets/avoid_checkpoint30000.pth
index 066b00180693a783ae134195e7cfdb1cd8975624..b6a0782cc1899a1e799011d19b3a9afb5906467c 100644
Binary files a/torch_training/Nets/avoid_checkpoint30000.pth and b/torch_training/Nets/avoid_checkpoint30000.pth differ
diff --git a/torch_training/multi_agent_inference.py b/torch_training/multi_agent_inference.py
index e8fd6d4cd607e23c2a32f4e789e5f800d9c0461e..c14672819b1c0fed58705725da6dfb1feb1b9872 100644
--- a/torch_training/multi_agent_inference.py
+++ b/torch_training/multi_agent_inference.py
@@ -16,7 +16,7 @@ from utils.observation_utils import normalize_observation
random.seed(3)
np.random.seed(2)
-
+"""
file_name = "./railway/complex_scene.pkl"
env = RailEnv(width=10,
height=20,
@@ -27,9 +27,9 @@ y_dim = env.height
"""
-x_dim = 10 # np.random.randint(8, 20)
-y_dim = 10 # np.random.randint(8, 20)
-n_agents = 5 # np.random.randint(3, 8)
+x_dim = 18 # np.random.randint(8, 20)
+y_dim = 14 # np.random.randint(8, 20)
+n_agents = 7 # np.random.randint(3, 8)
n_goals = n_agents + np.random.randint(0, 3)
min_dist = int(0.75 * min(x_dim, y_dim))
@@ -41,7 +41,7 @@ env = RailEnv(width=x_dim,
obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
number_of_agents=n_agents)
env.reset(True, True)
-"""
+
tree_depth = 3
observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestPathPredictorForRailEnv())
env_renderer = RenderTool(env, gl="PILSVG", )
@@ -53,7 +53,7 @@ for i in range(tree_depth + 1):
state_size = num_features_per_node * nr_nodes
action_size = 5
-n_trials = 1
+n_trials = 10
observation_radius = 10
max_steps = int(3 * (env.height + env.width))
eps = 1.
@@ -70,7 +70,7 @@ action_prob = [0] * action_size
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
agent = Agent(state_size, action_size, "FC", 0)
-with path(torch_training.Nets, "avoid_checkpoint52800.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint46200.pth") as file_in:
agent.qnetwork_local.load_state_dict(torch.load(file_in))
record_images = False
@@ -98,12 +98,11 @@ for trials in range(1, n_trials + 1):
for a in range(env.get_num_agents()):
action = agent.act(agent_obs[a], eps=0)
action_dict.update({a: action})
-
# Environment step
next_obs, all_rewards, done, _ = env.step(action_dict)
for a in range(env.get_num_agents()):
- agent_obs[a] = agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
+ agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
if done['__all__']:
break