Efficient Reinforcement Learning in Stochastic Environments Enabled by the Effective Horizon

Alapfogalmak

Many stochastic Markov Decision Processes can be efficiently solved by performing only a few steps of value iteration on the random policy's Q-function and then acting greedily.

Kivonat

The paper introduces a new reinforcement learning (RL) algorithm called SQIRL (shallow Q-iteration via reinforcement learning) that leverages the concept of the "effective horizon" to efficiently solve stochastic environments.
The key insights are:

Many stochastic MDPs can be approximately solved by performing only a few steps of value iteration on the random policy's Q-function and then acting greedily. This property is formalized as "k-QVI-solvability".
SQIRL alternates between collecting data through random exploration and then training function approximators to estimate the random policy's Q-function and perform a limited number of fitted Q-iteration steps.
SQIRL can use a wide variety of function approximators, including neural networks, as long as they satisfy basic in-distribution generalization properties.
The sample complexity of SQIRL is exponential only in the "stochastic effective horizon" - the minimum number of lookahead steps needed to solve the MDP - rather than the full horizon. This helps explain the success of deep RL algorithms that use random exploration and complex function approximation.
Empirically, SQIRL's performance strongly correlates with that of PPO and DQN in a variety of stochastic environments, supporting the theory that the effective horizon and SQIRL's approach can explain deep RL's practical successes.

Statisztikák

Many stochastic BRIDGE environments can be approximately solved by acting greedily with respect to the random policy's Q-function (k=1) or by applying just a few steps of Q-value iteration (2 ≤ k ≤ 5).
SQIRL solves about two-thirds as many sticky-action BRIDGE environments as PPO and nearly as many as DQN.
The empirical sample complexity of SQIRL has higher Spearman correlation with PPO and DQN than they do with each other.
SQIRL tends to have similar sample complexity to both PPO and DQN, typically performing about the same as DQN and slightly worse than PPO.

Idézetek

"Many stochastic MDPs can be efficiently solved by performing only a few steps of value iteration on the random policy's Q-function and then acting greedily."
"SQIRL alternates between collecting data through random exploration and then training function approximators to estimate the random policy's Q-function and perform a limited number of fitted Q-iteration steps."
"The sample complexity of SQIRL is exponential only in the 'stochastic effective horizon' - the minimum number of lookahead steps needed to solve the MDP - rather than the full horizon."

Főbb Kivonatok

The Effective Horizon Explains Deep RL Performance in Stochastic Environments

by Cassidy Laid... : arxiv.org 04-16-2024

https://arxiv.org/pdf/2312.08369.pdf

Mélyebb kérdések

How can the effective horizon and SQIRL's approach be extended to handle partial observability or other more complex environment dynamics

The effective horizon and SQIRL's approach can be extended to handle partial observability or other more complex environment dynamics by incorporating techniques such as belief state representation and POMDP solvers. In the case of partial observability, the agent maintains a belief state that captures the probability distribution over the true state of the environment given the history of observations and actions. By integrating this belief state into the Q-function estimation and policy improvement steps of SQIRL, the algorithm can effectively navigate environments with partial observability. Additionally, techniques like Monte Carlo Tree Search (MCTS) can be used to handle more complex dynamics by simulating possible future trajectories and updating the Q-function based on these simulations. By incorporating these methods, SQIRL can be adapted to handle a wider range of environments with varying levels of complexity and dynamics.

What are the limitations of the effective horizon assumption, and are there classes of stochastic environments where it does not hold

The effective horizon assumption has limitations in certain classes of stochastic environments where it may not hold. One limitation is in environments with high levels of uncertainty or noise, where the effective horizon may not accurately capture the lookahead required to make optimal decisions. Additionally, in environments with complex dynamics or non-stationarity, the effective horizon assumption may not hold as the optimal policy may require longer-term planning beyond the effective horizon. Furthermore, in environments with sparse rewards or deceptive dynamics, the effective horizon may not provide a reliable measure of the lookahead needed to achieve optimal performance. Overall, the effective horizon assumption is most effective in relatively simple and well-structured environments where limited lookahead can lead to near-optimal policies.

Can the insights from SQIRL be used to design new deep RL algorithms that are more sample-efficient and robust than existing methods

The insights from SQIRL can be used to design new deep RL algorithms that are more sample-efficient and robust than existing methods by focusing on the separation of exploration and learning components, leveraging regression for Q-function estimation, and incorporating limited lookahead planning. By building on the principles of SQIRL, new algorithms can be developed that efficiently learn near-optimal policies in stochastic environments by iteratively collecting data through exploration, estimating the Q-function using regression, and performing fitted Q-iteration for value iteration. Additionally, by incorporating techniques for handling partial observability, complex dynamics, and uncertainty, these new algorithms can achieve better sample efficiency and robustness in a wider range of environments. Furthermore, by exploring different regression techniques, function approximators, and exploration strategies, novel deep RL algorithms can be designed that build upon the success of SQIRL while addressing its limitations and extending its applicability to more challenging environments.