Update installation instructions.

README.md

@@ -24,13 +24,16 @@ Please note also that [ReBRAC](https://arxiv.org/abs/2305.09836) (developed conc
 
 ## Installation
 
+Linux installation with [miniconda](https://docs.anaconda.com/miniconda/miniconda-install/):
 ```bash
 git clone [email protected]:AdamJelley/EfficientOfflineRL.git && cd EfficientOfflineRL
 conda create -y -n EORL python=3.10
 conda activate EORL
 conda install -y -c conda-forge glew=2.1.0 glfw=3.3.10
-pip install -r requirements.txt
+pip install -r requirements/requirements.txt
 ```
+Note that this will install [MuJoCo](https://github.com/google-deepmind/mujoco), with all dependencies and binaries included. However, this installation will only work on Linux.
+
 Alternatively, you could use docker using the provided Dockerfile:
 ```bash
 docker build -t <image_name> .
@@ -55,7 +58,7 @@ Note these assume access to a CUDA device to run (otherwise set `--device cpu`).
 
 Other environments can be run by changing the config. Hyperparameters can be changed either in the relevant config or overridden with arguments as above.
 
-The pre-training argument can also be set to `--pretrain BC` to only pretrain the actor (corresponding to the ablation in Appendix D of the paper), or ignored to run the default algorithm without pre-training. LayerNorm can also be removed from the actor or critic with `--actor_LN False` and `--critic_LN False` respectively. For `pretrain_steps`, `td_component` (referred to as $\lambda$ in the paper) and other hyperparameters used please see the paper.
+The pre-training argument can also be set to `--pretrain BC` to only pretrain the actor (corresponding to the ablation in Appendix D of the paper), or ignored to run the default algorithm without pre-training. LayerNorm can also be removed from the actor or critic with `--actor_LN False` and `--critic_LN False` respectively. For `pretrain_steps`, `td_component` (referred to as $\lambda$ in the paper) and other hyperparameters used please see the paper. Videos of the agent during final evaluation can be recorded using `--render True`.
 
 ## Adroit Environments (Section 6 of Paper)
 

requirements/requirements.txt

@@ -1,11 +1,12 @@
-# Main dependencies
-git+https://github.com/tinkoff-ai/d4rl@master#egg=d4rl
-tqdm==4.64.0
-wandb==0.12.21
-mujoco-py==2.1.2.14
+git+https://github.com/Farama-Foundation/d4rl@master#egg=d4rl
+cython==0.29.33
+imageio[ffmpeg]==2.34.1
 numpy==1.23.1
-gym[mujoco_py,classic_control]==0.23.0
---extra-index-url https://download.pytorch.org/whl/cu113
-torch==1.11.0+cu113
-sortedcontainers==2.4.0
+pandas==2.2.2
+patchelf==0.17.2.1
+pre-commit==3.3.3
 pyrallis==0.3.1
+ruff==0.0.278
+torch==2.3.1
+tqdm==4.64.1
+wandb==0.13.10