• Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor
• Soft Actor-Critic
• Generalized State-Dependent Exploration
• Reverb: A framework for experience replay
• Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
• Acme: A Research Framework for Distributed Reinforcement Learning
• Dueling Network Architectures for Deep Reinforcement Learning
• Attention Is All You Need
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

1. Install dependences

   apt update -y
   apt install swig -y

2. Install RL-Toolkit

   pip3 install rl-toolkit[all]

3. Run (for Server)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 server

   Run (for Agent)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 agent --db_server localhost

   Run (for Learner)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 learner --db_server 192.168.1.2

   Run (for Tester)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 tester -f save/model/actor.h5

1. Install dependences
   Tensorflow for JetPack, follow instructions here for installation.

   sudo apt install swig -y

2. Install Reverb
   Download Bazel 3.7.2 for arm64, here

   mkdir ~/bin
   mv ~/Downloads/bazel-3.7.2-linux-arm64 ~/bin/bazel
   chmod +x ~/bin/bazel
   export PATH=$PATH:~/bin

   Clone Reverb with version that corespond with TF verion installed on NVIDIA Jetson !

   git clone https://github.com/deepmind/reverb
   cd reverb/
   git checkout r0.9.0

   Make changes in Reverb before building !
   In .bazelrc

   - build:manylinux2010 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010:toolchain
   + # build:manylinux2010 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010:toolchain
   
   - build --copt=-mavx --copt=-DEIGEN_MAX_ALIGN_BYTES=64
   + build --copt=-DEIGEN_MAX_ALIGN_BYTES=64

   In WORKSPACE

   - PROTOC_SHA256 = "15e395b648a1a6dda8fd66868824a396e9d3e89bc2c8648e3b9ab9801bea5d55"
   + # PROTOC_SHA256 = "15e395b648a1a6dda8fd66868824a396e9d3e89bc2c8648e3b9ab9801bea5d55"
   + PROTOC_SHA256 = "7877fee5793c3aafd704e290230de9348d24e8612036f1d784c8863bc790082e"

   In oss_build.sh

   -  bazel test -c opt --copt=-mavx --config=manylinux2010 --test_output=errors //reverb/cc/...
   +  bazel test -c opt --copt="-march=armv8-a+crypto" --test_output=errors //reverb/cc/...
   
   # Builds Reverb and creates the wheel package.
   -  bazel build -c opt --copt=-mavx $EXTRA_OPT --config=manylinux2010 reverb/pip_package:build_pip_package
   +  bazel build -c opt --copt="-march=armv8-a+crypto" $EXTRA_OPT reverb/pip_package:build_pip_package

   In reverb/cc/platform/default/repo.bzl

   urls = [
      -        "https://github.com/protocolbuffers/protobuf/releases/download/v%s/protoc-%s-linux-x86_64.zip" % (version, version),
      +        "https://github.com/protocolbuffers/protobuf/releases/download/v%s/protoc-%s-linux-aarch_64.zip" % (version, version),
   ]

   In reverb/pip_package/build_pip_package.sh

   -  "${PYTHON_BIN_PATH}" setup.py bdist_wheel ${PKG_NAME_FLAG} ${RELEASE_FLAG} ${TF_VERSION_FLAG} --plat manylinux2010_x86_64 > /dev/null
   +  "${PYTHON_BIN_PATH}" setup.py bdist_wheel ${PKG_NAME_FLAG} ${RELEASE_FLAG} ${TF_VERSION_FLAG}  > /dev/null

   Build and install

   bash oss_build.sh --clean true --tf_dep_override "tensorflow~=2.9.1" --release --python "3.8"
   bash ./bazel-bin/reverb/pip_package/build_pip_package --dst /tmp/reverb/dist/ --release
   pip3 install /tmp/reverb/dist/dm_reverb-*

   Cleaning

   cd ../
   rm -R reverb/

3. Install RL-Toolkit

   pip3 install rl-toolkit

4. Run (for Server)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 server

   Run (for Agent)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 agent --db_server localhost

   Run (for Learner)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 learner --db_server 192.168.1.2

   Run (for Tester)

   python3 -m rl_toolkit -c ./rl_toolkit/config.yaml -e MinitaurBulletEnv-v0 tester -f save/model/actor.h5

• SAC + gSDE + Huber loss
    is stored here, branch r2.0
• SAC + TQC + gSDE + LogCosh + Reverb
    is stored here, branch r4.0

Frameworks: Tensorflow, Reverb, OpenAI Gym, PyBullet, WanDB, OpenCV