diff --git a/README.md b/README.md
index 60170570eeee4b7c6fa00583293059f03abc11bd..95f833088ded6b33516f018457f16dd975af9f7c 100644
--- a/README.md
+++ b/README.md
@@ -1,101 +1,105 @@
-🚂 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 🚀
+🚂 This code is based on the official starter kit - NeurIPS 2020 Flatland Challenge
+---
-**🔗 [Train the single-agent DQN policy](https://flatland.aicrowd.com/getting-started/rl/single-agent.html)**
-**🔗 [Train the multi-agent DQN policy](https://flatland.aicrowd.com/getting-started/rl/multi-agent.html)**
+You can using for your own experiments
+```python
+set_action_size_full()
+```
+or
+```python
+set_action_size_reduced()
+```
+action space. The reduced action space removes DO_NOTHING.
-**🔗 [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! [](https://colab.research.google.com/drive/1GbPwZNQU7KJIJtilcGBTtpOAD3EabAzJ?usp=sharing)
-Sample training usage
---
+The policy interface has changed, please have a look into
-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:
```
-
-The multi-agent policy training can be tuned using command-line arguments:
-
-```console
-usage: multi_agent_training.py [-h] [-n N_EPISODES] [-t TRAINING_ENV_CONFIG]
- [-e EVALUATION_ENV_CONFIG]
- [--n_evaluation_episodes N_EVALUATION_EPISODES]
- [--checkpoint_interval CHECKPOINT_INTERVAL]
- [--eps_start EPS_START] [--eps_end EPS_END]
- [--eps_decay EPS_DECAY]
- [--buffer_size BUFFER_SIZE]
- [--buffer_min_size BUFFER_MIN_SIZE]
- [--restore_replay_buffer RESTORE_REPLAY_BUFFER]
- [--save_replay_buffer SAVE_REPLAY_BUFFER]
- [--batch_size BATCH_SIZE] [--gamma GAMMA]
- [--tau TAU] [--learning_rate LEARNING_RATE]
- [--hidden_size HIDDEN_SIZE]
- [--update_every UPDATE_EVERY]
- [--use_gpu USE_GPU] [--num_threads NUM_THREADS]
- [--render RENDER]
-
-optional arguments:
- -h, --help show this help message and exit
- -n N_EPISODES, --n_episodes N_EPISODES
- number of episodes to run
- -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
- evaluation config id (eg 0 for Test_0)
- --n_evaluation_episodes N_EVALUATION_EPISODES
- number of evaluation episodes
- --checkpoint_interval CHECKPOINT_INTERVAL
- checkpoint interval
- --eps_start EPS_START
- max exploration
- --eps_end EPS_END min exploration
- --eps_decay EPS_DECAY
- exploration decay
- --buffer_size BUFFER_SIZE
- replay buffer size
- --buffer_min_size BUFFER_MIN_SIZE
- min buffer size to start training
- --restore_replay_buffer RESTORE_REPLAY_BUFFER
- replay buffer to restore
- --save_replay_buffer SAVE_REPLAY_BUFFER
- save replay buffer at each evaluation interval
- --batch_size BATCH_SIZE
- minibatch size
- --gamma GAMMA discount factor
- --tau TAU soft update of target parameters
- --learning_rate LEARNING_RATE
- learning rate
- --hidden_size HIDDEN_SIZE
- hidden size (2 fc layers)
- --update_every UPDATE_EVERY
- how often to update the network
- --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
+tensorboard --logdir ./runs_bench
```
-[**📈 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)
-
-[](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)
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
---
diff --git a/run.py b/run.py
index 881d0b080cdd17a01d0acfbe41c06d92b3aebea0..239e18235ef6fe612ff00d0f257f2a827ac880da 100644
--- a/run.py
+++ b/run.py
@@ -1,4 +1,6 @@
'''
+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
@@ -104,8 +106,6 @@ if False:
checkpoint = None
EPSILON = 0.0
-# load_policy = "DeadLockAvoidance" # 22.13346834815911
-
# Use last action cache
USE_ACTION_CACHE = False