update readme

README.md

@@ -22,7 +22,11 @@
 
 ---
 
-OSRL (Offline Safe Reinforcement Learning) is an open-source implementation for offline safe reinforcement learning algorithms.
+**OSRL (Offline Safe Reinforcement Learning)** offers a collection of elegant and extensible implementations of state-of-the-art offline safe reinforcement learning (RL) algorithms. Aimed at propelling research in offline safe RL, OSRL serves as a solid foundation to implement, benchmark, and iterate on safe RL solutions.
+
+The OSRL package is a crucial component of our larger benchmarking suite for offline safe learning, which also includes [FSRL](https://github.com/liuzuxin/fsrl) and [DSRL](https://github.com/liuzuxin/dsrl), and is built to facilitate the development of robust and reliable offline safe RL solutions.
+
+To learn more, please visit our [project website](http://www.offline-saferl.org).
 
 ## Structure
 The structure of this repo is as follows:
@@ -38,48 +42,18 @@ The structure of this repo is as follows:
 ## Installation
 Pull the repo and install:
 ```
-git clone https://github.com/liuzuxin/offline-safe-rl-baselines.git
-cd offline-safe-rl-baselines
+git clone https://github.com/liuzuxin/osrl.git
+cd osrl
 pip install -e .
 ```
 
-## How to use DSRL
-DSRL uses the [OpenAI Gym](https://github.com/openai/gym) API. Tasks are created via the `gym.make` function. Each task is associated with a fixed offline dataset, which can be obtained with the `env.get_dataset()` method. This method returns a dictionary with:
-- `observations`: An N × obs_dim array of observations.
-- `next_observations`: An N × obs_dim of next observations.
-- `actions`: An N × act_dim array of actions.
-- `rewards`: An N dimensional array of rewards.
-- `costs`: An N dimensional array of costs.
-- `terminals`: An N dimensional array of episode termination flags. This is true when episodes end due to termination conditions such as falling over.
-- `timeouts`: An N dimensional array of termination flags. This is true when episodes end due to reaching the maximum episode length.
-
-```python
-import gym
-import dsrl
+## How to use OSRL
 
-# set seed
-seed = 0
+The example usage are in the `examples` folder, where you can find the training and evaluation scripts for all the algorithms.
 
-# Create the environment
-env = gym.make('OfflineCarCircle-v0')
+For example, to train the `bcql` method, simply run by overriding the default parameters:
 
-# dsrl abides by the OpenAI gym interface
-obs, info = env.reset(seed=seed)
-obs, reward, terminal, timeout, info = env.step(env.action_space.sample())
-cost = info["cost"]
-
-# Each task is associated with a dataset
-# dataset contains observations, next_observatiosn, actions, rewards, costs, terminals, timeouts
-dataset = env.get_dataset()
-print(dataset['observations']) # An N x obs_dim Numpy array of observations
+```shell
+python examples/train/train_bcql.py --param1 args1
 ```
-
-Datasets are automatically downloaded to the `~/.dsrl/datasets` directory when `get_dataset()` is called. If you would like to change the location of this directory, you can set the `$DSRL_DATASET_DIR` environment variable to the directory of your choosing, or pass in the dataset filepath directly into the `get_dataset` method.
-
-### Normalizing Scores
-- Set target cost by using `env.set_target_cost(target_cost)` function, where `target_cost` is the undiscounted sum of costs of an episode
-- You can use the `env.get_normalized_score(return, cost_return)` function to compute a normalized reward and cost for an episode, where `returns` and `cost_returns` are the undiscounted sum of rewards and costs respectively of an episode. 
-- The individual min and max reference returns are stored in `dsrl/infos.py` for reference.
-
-
-
+All the parameters and their default configs for each algorithm are available in the `examples/configs` folder.