README.md

-🚂 Starter Kit - NeurIPS 2020 Flatland Challenge
-===
+🚂 This code is based on the official starter kit - NeurIPS 2020 Flatland Challenge
+---
 
-This starter kit contains 2 example policies to get started with this challenge: 
-- a simple single-agent DQN method
-- a more robust multi-agent DQN method that you can submit out of the box to the challenge 🚀
+You can use for your own experiments full or reduced action space. 
+
+```python
+def map_action(action):
+    # if full action space is used -> no mapping required
+    if get_action_size() == get_flatland_full_action_size():
+        return action
+    
+    # if reduced action space is used -> the action has to be mapped to real flatland actions
+    # The reduced action space removes the DO_NOTHING action from Flatland.
+    if action == 0:
+        return RailEnvActions.MOVE_LEFT
+    if action == 1:
+        return RailEnvActions.MOVE_FORWARD
+    if action == 2:
+        return RailEnvActions.MOVE_RIGHT
+    if action == 3:
+        return RailEnvActions.STOP_MOVING
+```
 
-**🔗 [Train the single-agent DQN policy](https://flatland.aicrowd.com/getting-started/rl/single-agent.html)**
+```python
+set_action_size_full()
+```
+or 
+```python
+set_action_size_reduced()
+```
+action space. The reduced action space just removes DO_NOTHING. 
 
-**🔗 [Train the multi-agent DQN policy](https://flatland.aicrowd.com/getting-started/rl/multi-agent.html)**
+---
+The used policy is based on the FastTreeObs in the official starter kit - NeurIPS 2020 Flatland Challenge. But the
+ FastTreeObs in this repo is an extended version. 
+[fast_tree_obs.py](./utils/fast_tree_obs.py)
 
-**🔗 [Submit a trained policy](https://flatland.aicrowd.com/getting-started/first-submission.html)**
+---
+Have a look into the [run.py](./run.py) file. There you can select using PPO or DDDQN as RL agents. 
+ 
+```python
+####################################################
+# EVALUATION PARAMETERS
+set_action_size_full()
+
+# Print per-step logs
+VERBOSE = True
+USE_FAST_TREEOBS = True
+
+if False:
+    # -------------------------------------------------------------------------------------------------------
+    # RL solution
+    # -------------------------------------------------------------------------------------------------------
+    # 116591 adrian_egli
+    # graded	71.305	0.633	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/51
+    # Fri, 22 Jan 2021 23:37:56
+    set_action_size_reduced()
+    load_policy = "DDDQN"
+    checkpoint = "./checkpoints/210122120236-3000.pth"  # 17.011131341978228
+    EPSILON = 0.0
+
+if False:
+    # -------------------------------------------------------------------------------------------------------
+    # RL solution
+    # -------------------------------------------------------------------------------------------------------
+    # 116658 adrian_egli
+    # graded	73.821	0.655	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/52
+    # Sat, 23 Jan 2021 07:41:35
+    set_action_size_reduced()
+    load_policy = "PPO"
+    checkpoint = "./checkpoints/210122235754-5000.pth"  # 16.00113400887389
+    EPSILON = 0.0
+
+if True:
+    # -------------------------------------------------------------------------------------------------------
+    # RL solution
+    # -------------------------------------------------------------------------------------------------------
+    # 116659 adrian_egli
+    # graded	80.579	0.715	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/53
+    # Sat, 23 Jan 2021 07:45:49
+    set_action_size_reduced()
+    load_policy = "DDDQN"
+    checkpoint = "./checkpoints/210122165109-5000.pth"  # 17.993750197899438
+    EPSILON = 0.0
+
+if False:
+    # -------------------------------------------------------------------------------------------------------
+    # !! This is not a RL solution !!!!
+    # -------------------------------------------------------------------------------------------------------
+    # 116727 adrian_egli
+    # graded	106.786	0.768	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/54
+    # Sat, 23 Jan 2021 14:31:50
+    set_action_size_reduced()
+    load_policy = "DeadLockAvoidance"
+    checkpoint = None
+    EPSILON = 0.0
+```
 
-The single-agent example is meant as a minimal example of how to use DQN. The multi-agent is a better starting point to create your own solution.
+---
+A deadlock avoidance agent is implemented. The agent only lets the train take the shortest route. And it tries to avoid as many deadlocks as possible.
+* [dead_lock_avoidance_agent.py](./utils/dead_lock_avoidance_agent.py)
 
-You can fully train the multi-agent policy in Colab for free! [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GbPwZNQU7KJIJtilcGBTtpOAD3EabAzJ?usp=sharing)
 
-Sample training usage
 ---
+The policy interface has changed, please have a look into 
+* [policy.py](./reinforcement_learning/policy.py)
 
-Train the multi-agent policy for 150 episodes:
-
-```bash
-python reinforcement_learning/multi_agent_training.py -n 150
+---
+See the tensorboard training output to get some insights:
+```
+tensorboard --logdir ./runs_bench 
 ```
 
-The multi-agent policy training can be tuned using command-line arguments:
+---
+```
+python reinforcement_learning/multi_agent_training.py --use_fast_tree_observation  --checkpoint_interval 1000 -n 5000
+ --policy DDDQN -t 2 --action_size reduced --buffer_siz 128000
+```
 
-```console 
-usage: multi_agent_training.py [-h] [-n N_EPISODES] [-t TRAINING_ENV_CONFIG]
+[multi_agent_training.py](./reinforcement_learning/multi_agent_training.py)
+has new or changed parameters. Most important new or changed parameters for training. 
+ * policy :  [DDDQN, PPO, DeadLockAvoidance, DeadLockAvoidanceWithDecision, MultiDecision] : Default value
+   DeadLockAvoidance 
+ * use_fast_tree_observation : [false,true] : Default value = true  
+ * action_size: [full, reduced] : Default value = full
+``` 
+usage: multi_agent_training.py [-h] [-n N_EPISODES] [--n_agent_fixed]
+                               [-t TRAINING_ENV_CONFIG]
                                [-e EVALUATION_ENV_CONFIG]
                                [--n_evaluation_episodes N_EVALUATION_EPISODES]
                                [--checkpoint_interval CHECKPOINT_INTERVAL]
@@ -42,12 +144,16 @@ usage: multi_agent_training.py [-h] [-n N_EPISODES] [-t TRAINING_ENV_CONFIG]
                                [--hidden_size HIDDEN_SIZE]
                                [--update_every UPDATE_EVERY]
                                [--use_gpu USE_GPU] [--num_threads NUM_THREADS]
-                               [--render RENDER]
+                               [--render] [--load_policy LOAD_POLICY]
+                               [--use_fast_tree_observation]
+                               [--max_depth MAX_DEPTH] [--policy POLICY]
+                               [--action_size ACTION_SIZE]
 
 optional arguments:
   -h, --help            show this help message and exit
   -n N_EPISODES, --n_episodes N_EPISODES
                         number of episodes to run
+  --n_agent_fixed       hold the number of agent fixed
   -t TRAINING_ENV_CONFIG, --training_env_config TRAINING_ENV_CONFIG
                         training config id (eg 0 for Test_0)
   -e EVALUATION_ENV_CONFIG, --evaluation_env_config EVALUATION_ENV_CONFIG
@@ -82,20 +188,40 @@ optional arguments:
   --use_gpu USE_GPU     use GPU if available
   --num_threads NUM_THREADS
                         number of threads PyTorch can use
-  --render RENDER       render 1 episode in 100
-```
+  --render              render 1 episode in 100
+  --load_policy LOAD_POLICY
+                        policy filename (reference) to load
+  --use_fast_tree_observation
+                        use FastTreeObs instead of stock TreeObs
+  --max_depth MAX_DEPTH
+                        max depth
+  --policy POLICY       policy name [DDDQN, PPO, DeadLockAvoidance,
+                        DeadLockAvoidanceWithDecision, MultiDecision]
+  --action_size ACTION_SIZE
+                        define the action size [reduced,full]
+```                        
 
-[**📈 Performance training in environments of various sizes**](https://wandb.ai/masterscrat/flatland-examples-reinforcement_learning/reports/Flatland-Starter-Kit-Training-in-environments-of-various-sizes--VmlldzoxNjgxMTk)
 
-[**📈 Performance with various hyper-parameters**](https://app.wandb.ai/masterscrat/flatland-examples-reinforcement_learning/reports/Flatland-Examples--VmlldzoxNDI2MTA)
+---
+If you have any questions write me on the official discord channel **aiAdrian**    
+(Adrian Egli - adrian.egli@gmail.com) 
+
+
+Credits
+---
 
-[![](https://i.imgur.com/Lqrq5GE.png)](https://app.wandb.ai/masterscrat/flatland-examples-reinforcement_learning/reports/Flatland-Examples--VmlldzoxNDI2MTA) 
+* Florian Laurent <florian@aicrowd.com>
+* Erik Nygren <erik.nygren@sbb.ch>
+* Adrian Egli <adrian.egli@sbb.ch>
+* Sharada Mohanty <mohanty@aicrowd.com>
+* Christian Baumberger <christian.baumberger@sbb.ch>
+* Guillaume Mollard <guillaume.mollard2@gmail.com>
 
 Main links
 ---
 
 * [Flatland documentation](https://flatland.aicrowd.com/)
-* [NeurIPS 2020 Challenge](https://www.aicrowd.com/challenges/neurips-2020-flatland-challenge/)
+* [Flatland Challenge](https://www.aicrowd.com/challenges/flatland)
 
 Communication
 ---

run.py

 '''
+I did experiments in an early submission. Please note that the epsilon can have an
+effects on the evaluation outcome :
 DDDQNPolicy experiments - EPSILON impact analysis
 ----------------------------------------------------------------------------------------
 checkpoint = "./checkpoints/201124171810-7800.pth"  # Training on AGENTS=10 with Depth=2
@@ -51,44 +53,58 @@ set_action_size_full()
 # Print per-step logs
 VERBOSE = True
 USE_FAST_TREEOBS = True
-USE_PPO_AGENT = True
-
-# Checkpoint to use (remember to push it!)
-set_action_size_reduced()
-load_policy = "DeadLockAvoidanceWithDecision"
-checkpoint = "./checkpoints/210119075622-10000.pth"  # 22.13346834815911
-EPSILON = 0.0
-
-# Checkpoint to use (remember to push it!)
-set_action_size_reduced()
-load_policy = "PPO"
-checkpoint = "./checkpoints/210119134958-10000.pth"  # 12.18162927750207
-EPSILON = 0.0
-
-# Checkpoint to use (remember to push it!)
-#116591 adrian_egli
-# graded	71.305	0.633	RL	Successfully Graded ! More details about this submission can be found at:
-# http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/51
-# Fri, 22 Jan 2021 23:37:56
-set_action_size_reduced()
-load_policy = "DDDQN"
-checkpoint = "./checkpoints/210122120236-3000.pth"  # 17.011131341978228
-EPSILON = 0.0
 
-
-if True:
+if False:
+    # -------------------------------------------------------------------------------------------------------
+    # RL solution
+    # -------------------------------------------------------------------------------------------------------
+    # 116591 adrian_egli
+    # graded	71.305	0.633	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/51
+    # Fri, 22 Jan 2021 23:37:56
     set_action_size_reduced()
     load_policy = "DDDQN"
-    checkpoint = "./checkpoints/210122165109-5000.pth"  # 17.993750197899438
+    checkpoint = "./checkpoints/210122120236-3000.pth"  # 17.011131341978228
     EPSILON = 0.0
 
 if False:
+    # -------------------------------------------------------------------------------------------------------
+    # RL solution
+    # -------------------------------------------------------------------------------------------------------
+    # 116658 adrian_egli
+    # graded	73.821	0.655	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/52
+    # Sat, 23 Jan 2021 07:41:35
     set_action_size_reduced()
     load_policy = "PPO"
     checkpoint = "./checkpoints/210122235754-5000.pth"  # 16.00113400887389
     EPSILON = 0.0
 
-# load_policy = "DeadLockAvoidance" # 22.13346834815911
+if True:
+    # -------------------------------------------------------------------------------------------------------
+    # RL solution
+    # -------------------------------------------------------------------------------------------------------
+    # 116659 adrian_egli
+    # graded	80.579	0.715	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/53
+    # Sat, 23 Jan 2021 07:45:49
+    set_action_size_reduced()
+    load_policy = "DDDQN"
+    checkpoint = "./checkpoints/210122165109-5000.pth"  # 17.993750197899438
+    EPSILON = 0.0
+
+if False:
+    # -------------------------------------------------------------------------------------------------------
+    # !! This is not a RL solution !!!!
+    # -------------------------------------------------------------------------------------------------------
+    # 116727 adrian_egli
+    # graded	106.786	0.768	RL	Successfully Graded ! More details about this submission can be found at:
+    # http://gitlab.aicrowd.com/adrian_egli/neurips2020-flatland-starter-kit/issues/54
+    # Sat, 23 Jan 2021 14:31:50
+    set_action_size_reduced()
+    load_policy = "DeadLockAvoidance"
+    checkpoint = None
+    EPSILON = 0.0
 
 # Use last action cache
 USE_ACTION_CACHE = False