Deep Reinforcement Learning from Human Preferences. Deep Reinforcement Learning from Human Preferences.
Paper summary - explore RL systems with (non-expert) human preferences between pairs of trajectory segments; - run experiments on some RL tasks, namely **Atari** and **MuJoCo**, and show effectiveness of this approach; - advantages mentioned: - no need to access to the reward function; - less than 1% feedback needed -> reduce the cost of human oversight; - can learn complex novel behaviors. ## Introduction **Challenges** - goals complex, pooly-defined or hard to specify; - reward function -> behaviors that optimize reward function without achieving goals; > This difficulty underlines recent concerns about misalignment between reward values and the objectives of RL systems. **Alternatives** - inverse RL: extract a reward function from demonstrations of desired tasks; - imitation learning: clone the demonstrated behavior; *con: not applicable to behaviors that are hard to demonstrate for humans* - use human feedback as a reward function; *con: require thousands of hours of experience, prohibitively expensive* **Basic idea** https://i.imgur.com/3R7tO7R.png **Contributions** 1. solve tasks for which we can only recognize but not demonstrate the desired behaviors; 2. allow non-expert agent training; 3. scale to larger problems; 4. economical with user feedback. **Related work** two lines of work: (1) RL from human ratings or rankings; (2) general problemn of RL from preferences rather than absolute reward values. *close-related paper:* (1) [Active preference learning-based reinforcement learning](https://arxiv.org/abs/1208.0984); (2) [Programming by feedback](http://proceedings.mlr.press/v32/schoenauer14.pdf); (3) [A Bayesian approach for policy learning from trajectory preference queries](https://papers.nips.cc/paper/4805-a-bayesian-approach-for-policy-learning-from-trajectory-preference-queries). *diffs with (1)-(2)*: a) elicit preferences over whole trajectories rather than short clips; b) change training procedure to cope with nonlinear reward models and modern deep RL. *diffs with (3)*: a) fit reward function by Bayesian inference; b) produce trajectories using MAP estimate of the target policy instead of RL -> involve 'synthetic' human feedback drawn from Bayesian model. ## Preliminaries and Method **Agent goal** to produce trajectories which are preferred by the human, while making as few queries as possible to the human. **Work flow** at each point maintains two deep NNs - policy *pi*: O -> A; reward estimate *r\_hat*: O x A -> R. *Update procedure (asyn):* 1. policy *pi* => env => trajectories *tau* = {*tau^1*,..., *tau^i*}. Then update *pi* by a traditional RL algorithm to maximize the sum of predicted rewards *r\_t* = *r\_hat*(*o\_t*, *a\_t*); 2. select segment pairs *sigma* = (*sigma^1*, *sigma^2*) from *tau*. *sigma* => human comparison => labeled data; 3. update *r\_hat* with labeled data by supervised learning. > step 1 => trajectory *tau* => step 2 => human comparison => step 3 => parameters for *r\_hat* => step 1 => .... **Policy optimization (step 1)** *subtlety:* non-stationary reward function *r\_hat* -> methods robust to changes in reward function. A2C => Atari, TRPO => MuJoCo. use parameter settings that work well for traditional RL tasks; only adjust the entropy bonus for TRPO (improve inadequate exploration); normalize rewards to zero mean and constand std. **Preference eliciation (step 2)** clips of trajectory segments for 1 to 2 seconds long. *data struct:* triples (*sigma^1*, *sigma^2*, *mu*), *mu* - distribution over {1, 2}. one preferable over the others -> *mu* puts all mass on that choice; equally preferable -> *mu* uniform; incomparable -> skip saving triples. **Fitting the reward function (step 3)** *assumption:* human’s probability of preferring a segment *sigma^i* depends exponentially on the value of the latent reward summed over the length of the clip. https://i.imgur.com/2ViIWcL.png (no discount of reward <- human being indifferent about when things happen in the trajectory segment; could consider discounting.) https://i.imgur.com/cpdTZW6.png *modifications:* 1. ensemble of predictors -> independently normalize base predictors and then average results; 2. validation set ratio 1/e; employ *l\_2* regularization, and tune regularization coefficient -> validation loss = 1.1~1.5 training loss; dropout in some domains; 3. assume 10% chance that human responds uniformly at random; **Selecting queries** *pipeline:* sample trajectory segments of length k -> predict preference by base reward predictor in our ensemble -> select trajectories with the highest variance across ensemble members *future work:* query based on the expected value of information of query. *Related articles:* 1. [APRIL: Active Preference-learning based Reinforcement Learning](https://arxiv.org/abs/1208.0984) 2. [Active reinforcement learning: Observing rewards at a cost](http://www.filmnips.com/wp-content/uploads/2016/11/FILM-NIPS2016_paper_30.pdf) > At each time-step, the agent chooses both an action and whether to observe the reward in the next time-step. If the agent chooses to observe the reward, then it pays the “query cost” c > 0. The agent’s objective is to maximize total reward minus total query cost.

Summary by Tianxiao Zhao 3 months ago
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