added Docker instruction

Dockerfile

@@ -6,10 +6,10 @@
 #
 # 2. Start training:
 # docker build -f  Dockerfile -t img . && \
-# docker run -it --rm --gpus all -v $PWD:/workspace img \
+# docker run -it --rm --gpus all -v $PWD:/workspace -u $(id -u):$(id -g) img \
 #   sh xvfb_run.sh python3 dreamer.py \
-#   --logdir "./logdir/dmc_walker_walk" \
-#   --configs dmc_vision --task dmc_walker_walk
+#   --configs dmc_vision --task dmc_walker_walk \
+#   --logdir "./logdir/dmc_walker_walk"
 #
 # 3. See results:
 # tensorboard --logdir ~/logdir
@@ -34,11 +34,11 @@ ENV NUMBA_CACHE_DIR=/tmp
 # dmc setup
 RUN pip3 install tensorboard
 RUN pip3 install gym==0.19.0
-RUN pip3 install dm_control
+RUN pip3 install dm_control==1.0.9
 RUN pip3 install moviepy
 
 # crafter setup
-RUN pip3 install crafter
+RUN pip3 install crafter==1.8.0
 
 # atari setup
 RUN pip3 install atari-py==0.2.9

README.md

@@ -3,7 +3,9 @@ Pytorch implementation of [Mastering Diverse Domains through World Models](https
 
 ## Instructions
 
-Get dependencies:
+### Method 1: Manual
+
+Get dependencies with python 3.9:
 ```
 pip install -r requirements.txt
 ```
@@ -15,6 +17,9 @@ Monitor results:
 ```
 tensorboard --logdir ./logdir
 ```
+### Method 2: Docker
+
+Please refer to the Dockerfile for the instructions, as they are included within.
 
 ## Benchmarks
 So far, the following benchmarks can be used for testing.

exploration.py

@@ -58,9 +58,10 @@ class Plan2Explore(nn.Module):
             "feat": config.dyn_stoch + config.dyn_deter,
         }[self._config.disag_target]
         kw = dict(
-            inp_dim=feat_size + (config.num_actions
-            if config.disag_action_cond
-            else 0),  # pytorch version
+            inp_dim=feat_size
+            + (
+                config.num_actions if config.disag_action_cond else 0
+            ),  # pytorch version
             shape=size,
             layers=config.disag_layers,
             units=config.disag_units,

requirements.txt

@@ -1,7 +1,6 @@
 setuptools==60.0.0
 torch==2.0.0
 torchvision==0.15.1
-tensorboard==2.10.0
 pandas==1.2.4
 matplotlib==3.5.0
 ruamel.yaml==0.17.4
@@ -16,6 +15,7 @@ atari-py==0.2.9
 crafter==1.8.0
 opencv-python==4.7.0.72
 numpy==1.21.0
+tensorboard
 # minerl==0.4.4
 # This was needed for minerl
 # conda install -c conda-forge openjdk=8