Reinforcement Learning
The following examples demonstrate various functionalities of MLPro-RL:
- Howto 01 - (RL) Types of reward
- Howto 02 - (RL) Run agent with own policy with gym environment
- Howto 03 - (RL) Train agent with own policy on gym environment
- Howto 04 - (RL) Run multi-agent with own policy in multicartpole environment
- Howto 05 - (RL) Train multi-agent with own policy on multicartpole environment
- Howto 08 - (RL) Run own agents with petting zoo environment
- Howto 10 - (RL) Train using SB3 Wrapper
- Howto 11 - (RL) Wrap mlpro Environment class to gym environment
- Howto 12 - (RL) Wrap mlpro Environment class to petting zoo environment
- Howto 13 - (RL) Comparison Native and Wrapper SB3 Policy
- Howto 14 - (RL) Train UR5 with SB3 wrapper
- Howto 15 - (RL) Train Robothtm with SB3 Wrapper
- Howto 16 - (RL) Model Based Reinforcement Learning
- Howto 17 - (RL) Advanced training with stagnation detection
- Howto 18 - (RL) Single Agent with stagnation detection and SB3 Wrapper
- Howto 19 - (RL) Comparison Native and Wrapper SB3 Off-Policy
- Howto 20 - (RL) Train Multi Geometry with SB3 wrapper
- Howto 21 - (RL) Train and Load Single Agent
- Howto 22 - (RL) Train DoublePendulum with SB3 Wrapper