Reinforcement Learning

The following examples demonstrate various functionalities of MLPro-RL:

• Howto RL-001: Types of reward
• Howto RL-002: Run an agent with an own policy with an OpenAI GYM environment
• Howto RL-003: Train an agent with an own policy on an OpenAI Gym environment
• Howto RL-004: Run a multi-agent with an own policy with an OpenAI Gym environment
• Howto RL-005: Train a multi-agent with an own policy on the Multi-Cartpole environment
• Howto RL-006: Run own multi-agent with Petting Zoo environment
• Howto RL-007: Training of a wrapped Stable Baselines 3 policy
• Howto RL-008: Wrap native MLPro environment class to OpenAI Gym environment
• Howto RL-009: Wrap native MLPro environment class to PettingZoo environment
• Howto RL-010: Train a wrapped Stable Baslines 3 policy on MLPro’s native UR5 environment
• Howto RL-011: Train a wrapped Stable Baslines 3 policy on MLPro’s native UR5 environment (Paper)
• Howto RL-012: Train a wrapped Stable Baslines 3 policy on MLPro’s native RobotHTM environment
• Howto RL-013: Model Based Reinforcement Learning
• Howto RL-014: Advanced training with stagnation detection
• Howto RL-015: Train a wrapped Stable Baselines 3 policy with stagnation detection
• Howto RL-016: Comparison of native and wrapped Stable Baselines 3 policy
• Howto RL-017: Comparison of native and wrapped Stable Baselines 3 policy (off-policy)
• Howto RL-018: Train a wrapped Stable Baselines 3 policy on MLPro’s native MultiGeo environment
• Howto RL-019: Train and reuse a single agent
• Howto RL-020: Run a native random agent in MLPro’s native DoublePendulum environment
• Howto RL-021: Train a wrapped Stable Baselines 3 policy on MLPro’s native DoublePendulum environment