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Benchmarking Tabular Reinforcement Learning Algorithms
- The article explores benchmarking tabular reinforcement learning algorithms based on the seminal book by Sutton and Barto, focusing on Dynamic Programming, Monte Carlo methods, Temporal Difference Learning, and Model-Based RL/Planning.
- The benchmarking framework involves evaluating algorithms on increasingly larger Gridworld environments to compare their efficiency and effectiveness in solving tasks.
- Key algorithms like Q-learning, value iteration, Sarsa, and Dyna-Q are discussed and benchmarked on Gridworlds up to size 50x50.
- Results indicate that Value Iteration outperforms other methods, followed by On-policy MC, Dyna-Q, Q-learning, and Sarsa-n in the context of simple, fully-known, and deterministic environments.
- Performance rankings show On-policy MC as the best model-free algorithm, while Dyna-Q blends model-based planning with model-free learning for improved results.
- The article discusses the trade-off between efficiency and generality in reinforcement learning algorithms, with future plans to benchmark in more challenging environments and explore function approximation in Part II of Sutton's book.
- The detailed experiments, rankings, and discussions offer insights into the performance of tabular RL algorithms, showcasing the strengths and limitations of each method.
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