Reinforcement Learning - chunhualiao/public-docs GitHub Wiki
Why interesting and important?
- a promising path to super-human intelligience
Learn by doing example projects RL:projects
- reinforcement learning:report
- Reinforcement learning with verifiable rewards RLVR
When designing a reinforcement learning (RL) training system, several essential components must be carefully considered to ensure effective learning and performance. Below is a breakdown of the key components:
TD stands for Temporal Difference.
- Temporal Difference learning is a class of reinforcement learning algorithms that learn from the difference in the estimated values of states over time. The "TD error" represents the difference between the updated value estimate (based on the observed reward and the value of the succeeding state) and the previously estimated value.
reinforcement learning:suitable problems
- Definition: The environment is the world in which the agent operates. It defines the rules, dynamics, and interactions between the agent and its surroundings.
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Key Considerations:
- State representation (e.g., continuous, discrete, or mixed).
- Action space (e.g., discrete actions, continuous control).
- Reward function (e.g., sparse, dense, or shaped rewards).
- Termination conditions (e.g., episode length, success/failure criteria).
- Definition: The agent is the learner or decision-maker that interacts with the environment.
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Key Components:
- Policy: A function that maps states to actions (e.g., deterministic or stochastic).
- Value Function: Estimates the expected cumulative reward from a given state or state-action pair.
- Model (optional): A representation of the environment's dynamics (used in model-based RL).
- Definition: The reward signal provides feedback to the agent about the success or failure of its actions.
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Key Considerations:
- Reward shaping to guide learning.
- Sparse vs. dense rewards (e.g., rewards at every step vs. only at the end of an episode).
- Avoiding reward hacking (e.g., unintended behaviors that maximize rewards but don't solve the task).
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State Space: The set of all possible states the environment can be in.
- Can be discrete, continuous, or hybrid.
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Action Space: The set of all possible actions the agent can take.
- Can be discrete (e.g., selecting from a list of actions) or continuous (e.g., controlling a robot arm).
- Exploration: The agent tries new actions to discover their effects and potentially find better strategies.
- Exploitation: The agent uses its current knowledge to maximize rewards.
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Balancing Mechanisms:
- Epsilon-greedy: Randomly explores with probability ε.
- Softmax: Selects actions based on a probability distribution.
- Thompson Sampling: Uses Bayesian methods for exploration.
- Intrinsic Motivation: Encourages exploration through curiosity or novelty.
- Definition: The algorithm used to update the agent's policy or value function based on experience.
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Types:
- Value-Based Methods: Learn a value function (e.g., Q-learning, Deep Q-Networks).
- Policy-Based Methods: Directly optimize the policy (e.g., REINFORCE, Policy Gradients).
- Actor-Critic Methods: Combine value-based and policy-based approaches (e.g., A3C, PPO).
- Model-Based Methods: Learn a model of the environment and plan using it (e.g., Dyna-Q).
- Definition: A buffer that stores past experiences (state, action, reward, next state) for training.
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Purpose:
- Improves sample efficiency by reusing past experiences.
- Stabilizes training by breaking correlations between consecutive samples.
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Variants:
- Prioritized Experience Replay: Prioritizes important or rare experiences.
- Definition: Used to approximate the policy, value function, or model in high-dimensional or continuous spaces.
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Key Considerations:
- Architecture design (e.g., feedforward, convolutional, recurrent).
- Regularization techniques (e.g., dropout, weight decay).
- Optimization algorithms (e.g., Adam, RMSProp).
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Steps:
- The agent interacts with the environment to collect experience.
- The experience is used to update the agent's policy or value function.
- The process repeats until convergence or a stopping criterion is met.
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Key Considerations:
- Batch size and update frequency.
- Synchronous vs. asynchronous training (e.g., A3C).
- Definition: Assessing the agent's performance during and after training.
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Key Components:
- Metrics: Cumulative reward, success rate, episode length, etc.
- Visualization: Learning curves, action distributions, etc.
- Debugging: Identifying issues like reward hacking, overfitting, or exploration failures.
- Definition: Parameters that control the learning process but are not learned by the agent.
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Examples:
- Learning rate.
- Discount factor (gamma).
- Exploration rate (epsilon).
- Batch size.
- Network architecture (e.g., number of layers, hidden units).
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Simulation:
- High-fidelity simulators for training in safe, controlled environments.
- Transfer learning to adapt from simulation to the real world.
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Real-World:
- Safety constraints (e.g., avoiding catastrophic actions).
- Sample efficiency (e.g., minimizing the number of interactions with the real environment).
- Definition: Techniques to speed up training and handle large-scale problems.
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Approaches:
- Distributed training (e.g., using multiple workers or GPUs).
- Asynchronous methods (e.g., A3C, IMPALA).
- Definition: Comparing the agent's performance against standard benchmarks or simple baselines.
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Purpose:
- Ensures the agent is learning effectively.
- Provides a reference point for improvement.
By carefully designing and integrating these components, you can create a robust and efficient reinforcement learning system tailored to your specific problem domain.