Update README.md

README.md

@@ -6,13 +6,48 @@ language:
 tags:
 - reinforcement-learning
 - deep-reinforcement-learning
-- robotics
 - pytorch
 - gymnasium
 - collision-avoidance
 - navigation
 - self-driving
 - autonomous-vehicle
+- github:kengboon/RoadEnv
 ---
 
-This repository contains model weights for the agents performing in [RoadEnv](https://github.com/kengboon/RoadEnv).
+This repository contains model weights for the agents performing in [RoadEnv](https://github.com/kengboon/RoadEnv).
+
+## Models
+- Recurrent Soft Actor-Critic (RSAC/SAC-LSTM) [[Agent](https://github.com/kengboon/RoadEnv/blob/main/rl_algorithms2/sac_v2_lstm.py)] [[Training](https://github.com/kengboon/RoadEnv/blob/main/scripts/training-sac_v2-lstm-2.py)] [[Test](https://github.com/kengboon/RoadEnv/blob/main/scripts/test-sac_v2_lstm.py)]
+- Recurrent Soft Actor-Critic Share (RSAC-Share) [[Paper](https://arxiv.org/abs/2110.12628)] [[Agent](https://github.com/kengboon/RoadEnv/blob/main/rl_algorithms2/sac_v2_lstm_share.py)] [[Training](https://github.com/kengboon/RoadEnv/blob/main/scripts/training-rsac_share.py)]
+- Soft Actor-Critic (SAC) [[Agent](https://github.com/kengboon/RoadEnv/blob/main/rl_algorithms2/sac_v2.py)] [[Training](https://github.com/kengboon/RoadEnv/blob/main/scripts/training-sac_v2-2.py)] [[Test](https://github.com/kengboon/RoadEnv/blob/main/scripts/test-sac_v2.py)]
+
+## Usage
+```Python
+# Register environment
+from road_env import register_road_envs
+register_road_envs()
+
+# Make environment
+import gymnasium as gym
+env = gym.make('urban-road-v0', render_mode='rgb_array')
+
+# Configure parameters (example)
+env.configure({
+    "random_seed": None,
+    "duration": 60,
+})
+
+obs, info = env.reset()
+
+# Graphic display
+import matplotlib.pyplot as plt
+plt.imshow(env.render())
+
+# Execution
+done = truncated = False
+while not (done or truncated):
+    action = ... # Your agent code here
+    obs, reward, done, truncated, info = env.step(action)
+    env.render() # Update graphic
+```